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Objectives: Social support (SS) and social isolation (SI) are social determinants of health (SDOH) associated with psychiatric outcomes. In electronic health records (EHRs), individual-level SS/SI is typically documented in narrative clinical notes rather than as structured coded data. Natural language processing (NLP) algorithms can automate the otherwise labor-intensive process of extraction of such information., Materials and Methods: Psychiatric encounter notes from Mount Sinai Health System (MSHS, n = 300) and Weill Cornell Medicine (WCM, n = 225) were annotated to create a gold-standard corpus. A rule-based system (RBS) involving lexicons and a large language model (LLM) using FLAN-T5-XL were developed to identify mentions of SS and SI and their subcategories (eg, social network, instrumental support, and loneliness)., Results: For extracting SS/SI, the RBS obtained higher macroaveraged F1-scores than the LLM at both MSHS (0.89 versus 0.65) and WCM (0.85 versus 0.82). For extracting the subcategories, the RBS also outperformed the LLM at both MSHS (0.90 versus 0.62) and WCM (0.82 versus 0.81)., Discussion and Conclusion: Unexpectedly, the RBS outperformed the LLMs across all metrics. An intensive review demonstrates that this finding is due to the divergent approach taken by the RBS and LLM. The RBS was designed and refined to follow the same specific rules as the gold-standard annotations. Conversely, the LLM was more inclusive with categorization and conformed to common English-language understanding. Both approaches offer advantages, although additional replication studies are warranted., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Importance: Diagnostic codes in the Electronic Health Record (EHR) are known to be limited in reporting patient suicidality, and especially in differentiating the levels of suicide severity., Objective: The authors developed and validated a portable natural language processing (NLP) algorithm for detection of suicidal ideation (SI) and suicide-related behavior and attempts (SB/SA) in EHR data. The algorithm was then deployed, and SI and SB/SA ascertainment was compared to that of International Statistical Classification of Diseases (ICD-9 and 10) diagnostic codes., Design: A group of experts designed the pipeline to detect and distinguish suicide severity based on the Columbia-Suicide Severity Rating Scale (C-SSRS). Notes were manually annotated to create the "Gold Standard" with which the algorithm output was evaluated for accuracy., Setting: The algorithm was developed at two academic medical centers, Weill Cornell Medicine (WCM), the Mount Sinai Health System (MSHS), and tested at these two, plus a third, the University of Utah Healthcare Center (UUHSC)., Participants: Notes were from participants with psychiatric encounters at the three institutions., Main Outcomes: The two main outcomes were the accuracy scores of the NLP pipeline and comparison of ascertainment rates to ICD codes., Results: F1 accuracy scores ranged from 0.86-0.97 at the three sites. The NLP rate of detection of SB/SA was almost 30 times higher, and SI was almost 10 times higher, when compared with that of diagnostic codes. NLP detected almost all cases detected by diagnostic codes. No bias in performance was found for race/ethnicity and performance was comparable in psychiatric and non-psychiatric EHRs., Conclusions and Relevance: EHRs from cohorts with psychiatric diagnoses or encounters at WCM, MSHS, and UUHSC had SI and SB/SA extracted using an NLP algorithm based on parameters defined by the C-SSRS. Validity was determined by comparing the algorithm output to manual annotations of clinical notes by domain experts. NLP-detection of SI and SB/SA was compared with that of ICD codes across a range of demographic groups. Algorithm performance was also examined for bias in minoritized groups and in non-psychiatric notes., Key Points: Question: Can we automate the extraction of data available in clinical notes to accurately detect and distinguish patients with suicidal ideation (SI) and suicidal behavior (SB)? Findings: Our Natural Language Processing (NLP) approach was able to identify and distinguish SI and SB at three different hospital systems with benchmarked accuracy scores (above 0.85). The rate of detecting SI and SB using the algorithm was 10-30 times that of diagnostic codes found in the Electronic Health Record. Meaning: Our algorithm renders the use of International Classification of Disease (ICD) diagnostic codes for SI and SB ascertainment obsolete.

Insecticide resistance is a global concern and requires immediate attention to manage dreadful insect pests. One of the resistance mechanisms adopted by insects is through ATP-binding cassette (ABC) transporter proteins. These proteins rapidly transport and eliminate the insecticidal molecules across the lipid membranes (Phase III detoxification mechanism). In the present study, we investigated the potential role of ABC transporter genes in imparting insecticide resistance in field-collected insecticide resistant larvae of eggplant shoot and fruit borer (Leucinodes orbonalis; Crambidae: Lepidoptera). Dose-mortality bioassays against five insecticidal molecules revealed moderate to high levels of insecticide resistance (32.2. to 134.1-fold). Thirty-one genes encoding ABC transporter proteins were mined from the transcriptome resources of L. orbonalis. They were classified under eight sub-families (ABCA to ABCH). Phylogenetic analysis indicated ABCG is the most divergent, composed of nine genes as compared to many other insects. Transcriptome analysis of the insecticide resistant and susceptible strains of L. orbonalis revealed differential expression of 13 ABC transporter genes. The altered expression of these genes was further validated using qRT-PCR. The knockdown studies indicated the involvement of ABCD1 and ABCG2 genes in chlorantraniliprole resistance in the insecticide-resistant strain of L. orbonalis. This study unveils the additional mechanisms employed by L. orbonalis in resisting insecticide toxicity through accelerated excretion mode. These ABCD and ABCG family genes could be candidate targets in developing genome-assisted pest management strategies in the future., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Treatment resistance is common in major depressive disorder (MDD), yet clinical risk factors are not well understood. Using a discovery-replication design, we conducted phenome-wide association studies (PheWASs) of MDD treatment resistance in two electronic health record (EHR)-linked biobanks. The PheWAS included participants with an MDD diagnosis in the EHR and at least one antidepressant (AD) prescription. Participant lifetime diagnoses were mapped to phecodes. PheWASs were conducted for three treatment resistance outcomes based on AD prescription data: number of unique ADs prescribed, ≥1 and ≥2 CE switches. Of the 180 phecodes significantly associated with these outcomes in the discovery cohort (n = 12,558), 71 replicated (n = 8,206). In addition to identifying known clinical factors for treatment resistance in MDD, the total unique AD prescriptions was associated with additional clinical variables including irritable bowel syndrome, gastroesophageal reflux disease, symptomatic menopause, and spondylosis. We calculated polygenic risk of specific-associated conditions and tested their association with AD outcomes revealing that genetic risk for many of these conditions is also associated with the total unique AD prescriptions. The number of unique ADs prescribed, which is easily assessed in EHRs, provides a more nuanced measure of treatment resistance, and may facilitate future research and clinical application in this area., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Mann receives royalties for commercial use of the C-SSRS from the Research Foundation for Mental Hygiene and from Columbia University from sales of the Columbia Pathways app. Dr. Weissman has received funding from NIMH and Columbia University Institute for Developmental Sciences and receives book royalties from Perseus Press and Oxford Press. None of these represent a conflict of interest. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Maconellicoccus hirsutus is a highly polyphagous insect pest, posing a substantial threat to various crop sp., especially in the tropical and sub-tropical regions of the world. While extensive physiological and biological studies have been conducted on this pest, the lack of genetic information has hindered our understanding of the molecular mechanisms underlying its growth, development, and xenobiotic metabolism. The Cytochrome P450 gene, a member of the CYP gene superfamily ubiquitous in living organisms is associated with growth, development, and the metabolism of both endogenous and exogenous substances, contributing to the insect's adaptability in diverse environments. To elucidate the specific role of the CYP450 gene family in M. hirsutus which has remained largely unexplored, a de novo transcriptome assembly of the pink mealybug was constructed. A total of 120 proteins were annotated as CYP450 genes through homology search of the predicted protein sequences across different databases. Phylogenetic studies resulted in categorizing 120 CYP450 genes into four CYP clans. A total of 22 CYP450 families and 30 subfamilies were categorized, with CYP6 forming the dominant family. The study also revealed five genes (Halloween genes) associated with the insect hormone biosynthesis pathway. Further, the expression of ten selected CYP450 genes was studied using qRT-PCR across crawler, nymph, and adult stages, and identified genes that were expressed at specific stages of the insects. Thus, the findings of this study reveal the expression dynamics and possible function of the CYP450 gene family in the growth, development, and adaptive strategies of M. hirsutus which can be further functionally validated., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: Artificial intelligence (AI) is being developed for mental healthcare, but patients' perspectives on its use are unknown. This study examined differences in attitudes towards AI being used in mental healthcare by history of mental illness, current mental health status, demographic characteristics, and social determinants of health., Methods: We conducted a cross-sectional survey of an online sample of 500 adults asking about general perspectives, comfort with AI, specific concerns, explainability and transparency, responsibility and trust, and the importance of relevant bioethical constructs., Results: Multiple vulnerable subgroups perceive potential harms related to AI being used in mental healthcare, place importance on upholding bioethical constructs, and would blame or reduce trust in multiple parties, including mental healthcare professionals, if harm or conflicting assessments resulted from AI., Discussion: Future research examining strategies for ethical AI implementation and supporting clinician AI literacy is critical for optimal patient and clinician interactions with AI in mental healthcare., Competing Interests: MRT, JP, and AH are co-founders and have equity in Iris OB Health, New York. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Reading Turchioe, Desai, Harkins, Kim, Kumar, Zhang, Joly, Pathak, Hermann and Benda.)

Despite the high correlation between anxiety and depression, little remains known about the course of each condition when presenting concurrently. This study aimed to identify longitudinal patterns during antidepressant treatment in patients with depression and anxiety, and evaluate related factors associated with these patterns. By analyzing longitudinal self-report Patient Health Questionnaire-9 (PHQ-9) and General Anxiety Disorder-7 (GAD-7) scores that tracked courses of depression and anxiety over a three-month window among the 577 adult participants, six depression and six anxiety trajectory subgroups were computationally derived using group-based trajectory modeling. Three depression subgroups showed symptom improvement, while three showed nonresponses. Similar patterns were observed in the six anxiety subgroups. Multinomial regression was used to associate patient characteristics with trajectory subgroup membership. Compared to patients in the remission group, factors associated with depressive symptom nonresponse included older age and lower depression severity., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The pink or hibiscus mealybug, Maconellicoccus hirsutus, is a serious pest of grapes, jute, and mesta, causing severe yield losses in India and other countries. Chemical control remains the foremost choice for farmers to manage this pest. As insecticides break down over time due to biotic and abiotic factors, insects are exposed to varying levels of these exogenous compounds. Several studies have reported that sublethal doses affect insect physiology, but only a few have examined the changes in gene expression at the molecular level. Therefore, the present study was conducted to elucidate the molecular mechanisms in M. hirsutus exposed to sublethal doses of buprofezin 25 SC. Life table analysis revealed increased fecundity in M. hirsutus exposed to the sublethal dose. A total of 1,744 differentially expressed genes were identified between the buprofezin-treated and untreated samples using transcriptome analysis. These genes were primarily associated with ribosomal proteins, proteases, cuticular proteins, and cytoskeletal structures. Ribosomes and phagosomes were the most highly enriched pathways. Interestingly, most of the DEGs were involved in restoring homeostasis rather than detoxification. To validate our RNA-sequencing results, qRT-PCR validation was performed on ten randomly selected genes. Overall, our findings provide valuable insights into intermittent changes in stress-coping genes, apart from detoxification genes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with suicides. Research is limited in automatic identification of such data from clinical notes in Electronic Health Records. This study developed deep learning (DL) tools utilizing transformer models (Bio_ClinicalBERT and GatorTron) to detect PSH and FSH in clinical notes derived from three academic medical centers, and compared their performance with a rule-based natural language processing tool. For detecting PSH, the rule-based approach obtained an F1-score of 0.75 ± 0.07, while the Bio_ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based approach achieved an F1-score of 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio_ClinicalBERT and 0.92 ± 0.07 for GatorTron. Across sites, the DL tools identified more than 80% of patients at elevated risk for suicide who remain undiagnosed and untreated., (© 2024. The Author(s).)

Background: The application of artificial intelligence (AI) to health and health care is rapidly increasing. Several studies have assessed the attitudes of health professionals, but far fewer studies have explored the perspectives of patients or the general public. Studies investigating patient perspectives have focused on somatic issues, including those related to radiology, perinatal health, and general applications. Patient feedback has been elicited in the development of specific mental health care solutions, but broader perspectives toward AI for mental health care have been underexplored., Objective: This study aims to understand public perceptions regarding potential benefits of AI, concerns about AI, comfort with AI accomplishing various tasks, and values related to AI, all pertaining to mental health care., Methods: We conducted a 1-time cross-sectional survey with a nationally representative sample of 500 US-based adults. Participants provided structured responses on their perceived benefits, concerns, comfort, and values regarding AI for mental health care. They could also add free-text responses to elaborate on their concerns and values., Results: A plurality of participants (245/497, 49.3%) believed AI may be beneficial for mental health care, but this perspective differed based on sociodemographic variables (all P<.05). Specifically, Black participants (odds ratio [OR] 1.76, 95% CI 1.03-3.05) and those with lower health literacy (OR 2.16, 95% CI 1.29-3.78) perceived AI to be more beneficial, and women (OR 0.68, 95% CI 0.46-0.99) perceived AI to be less beneficial. Participants endorsed concerns about accuracy, possible unintended consequences such as misdiagnosis, the confidentiality of their information, and the loss of connection with their health professional when AI is used for mental health care. A majority of participants (80.4%, 402/500) valued being able to understand individual factors driving their risk, confidentiality, and autonomy as it pertained to the use of AI for their mental health. When asked who was responsible for the misdiagnosis of mental health conditions using AI, 81.6% (408/500) of participants found the health professional to be responsible. Qualitative results revealed similar concerns related to the accuracy of AI and how its use may impact the confidentiality of patients' information., Conclusions: Future work involving the use of AI for mental health care should investigate strategies for conveying the level of AI's accuracy, factors that drive patients' mental health risks, and how data are used confidentially so that patients can determine with their health professionals when AI may be beneficial. It will also be important in a mental health care context to ensure the patient-health professional relationship is preserved when AI is used., (©Natalie Benda, Pooja Desai, Zayan Reza, Anna Zheng, Shiveen Kumar, Sarah Harkins, Alison Hermann, Yiye Zhang, Rochelle Joly, Jessica Kim, Jyotishman Pathak, Meghan Reading Turchioe. Originally published in JMIR Mental Health (https://mental.jmir.org), 18.09.2024.)

Leukocyte cell-derived chemotaxin-2 (LECT2), originally identified as a novel neutrophil chemokine, is a multifunctional secreted factor primarily produced in hepatocytes. However, many studies have shown that LECT2 is a pleiotropic protein that not only exerts chemotaxis properties as a cytokine but also plays an important role in inflammatory regulation and immune regulation. Pathogens such as bacteria and the role of the host immune system are key factors in the inflammatory response. In antibacterial, LECT2 can directly destroy bacterial structure or affect the normal metabolism of bacteria to inactivate bacteria and can also achieve this effect by activating immune cells and regulating cytokines. In immunomodulation, LECT2 has neutrophil chemotactic activity and regulates the quantities of Natural killer T (NKT) cells, regulatory T cells, monocytes/macrophages, granulocytes, and/or the expression of associated cytokines, thereby influencing their effect in immune reaction. Inflammation and immune regulation are closely related to a variety of diseases, such as bacterial infection, liver cirrhosis, dermatitis, coronary atherosclerotic heart disease, and so on. This review summarizes the basic and clinical studies of LECT2 in antibacterial effects and its effects on immune cells to explore the mechanism of LECT in inflammatory regulation and immune regulation in physiological and pathological conditions better., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: The study aims to quantify differential trends in emergency, outpatient, and inpatient healthcare service utilization among among 2,540,348 patients with and without severe mental illness (SMI) before (January 2017 - December 2019) and during (January 2020 - April 2022) the COVID-19 pandemic, across three distinct cohorts tracked from January 2017 to April 2022, based on their SMI status., Design & Setting: A retrospective cohort study was conducted, utilizing data from Healthix, the second-largest health information exchange in the U.S., Participants: The study population included 2,540,348 patients were categorized by their SMI status as of January 2017 to December 2019 into three groups: Severe Mental Illness 137,801 (5.4%), Other Mental Illness 685,280 (27%), No Mental Illness 1,717,267 (67.6%)., Exposures: The primary exposure was the COVID-19 pandemic, with a focus on its impact on mental health and non mental health services., Main Outcomes and Measures: The primary outcome was the rate of utilization of mental health and non mental health services., Results: Among the non-SMI patients, there was a 30% decline in emergency visits from 650,000 pre-COVID to 455,000 post-COVID (OR=0.70, p < 0.001), and outpatient visits decreased by 50% from 1.2 million to 600,000 (OR=0.50, p = 0.002). In contrast, the SMI group witnessed a 20% reduction in outpatient visits from 120,000 to 96,000 (OR=0.80, p = 0.015) and a 40% decrease in inpatient visits from 50,000 to 30,000 (OR=0.60, p = 0.008). Recurrent SMI patients exhibited a 25% decline in emergency visits from 32,000 to 24,000 (OR=0.75, p = 0.03) and a 35% drop in outpatient visits from 40,000 to 26,000 (OR=0.65, p = 0.009).The pandemic influenced the type of disorders diagnosed. Non-SMI patients experienced a 23% rise in anxiety-related disorders (n=80,000, OR=1.23, p = 0.01) and an 18% increase in stress-related disorders (n=70,000, OR=1.18, p = 0.04). SMI patients had a 15% surge in severe anxiety disorders (n=9,000, OR=1.15, p = 0.02) and a 12% uptick in substance-related disorders (n=7,200, OR=1.12, p = 0.05). Recurrent SMI patients showed a 20% increase in anxiety and adjustment disorders (n=6,400, OR=1.20, p = 0.03).SMI patients were more adversely affected by COVID-19, with a higher infection rate of 7.8% (n=45,972) compared to 4.2% (n=131,669) in non-SMI patients (OR=1.88, p < 0.001). Hospitalization rates also followed this trend, with 5.2% (n=30,648) of SMI patients being hospitalized compared to 3.7% (n=115,995) among non-SMI patients (OR=1.41, p = 0.007). Moreover, SMI patients had lower vaccination rates of 45.6% (n=268,888) versus 58.9% (n=1,844,261) among non-SMI patients (OR=0.77, p = 0.019)., Conclusions: In conclusion, our findings reveal significant disparities in healthcare service utilization between individuals with Serious Mental Illness (SMI) and those without. Notably, the SMI cohort experienced greater disruptions in service continuity, with a more pronounced decline in both outpatient and inpatient visits. Furthermore, the types of disorders diagnosed among this group also saw a shift, emphasizing the need for specialized care and attention during times of crisis. The higher rates of COVID-19 infection and hospitalization among SMI patients compared to non-SMI patients underscore the urgency of targeted public health interventions for this vulnerable group. The lower vaccination rates in the SMI cohort highlight another layer of healthcare disparity that needs to be urgently addressed. These findings suggest that the pandemic has amplified pre-existing inequalities in healthcare access and outcomes for individuals with SMI, calling for immediate, evidence-based interventions to mitigate these effects and ensure equitable healthcare service provision.

The broad mite, Polyphagotarsonemus latus (Tarsonemidae: Acari) is a highly polyphagous species that damage plant species spread across 57 different families. This pest has developed high levels of resistance to some commonly used acaricides. In the present investigation, we deciphered the genome information of P. latus by PacBio HiFi sequencing. P. latus is the third smallest arthropod genome sequenced so far with a size of 49.1 Mb. The entire genome was assembled into two contigs. A set of 9,286 protein-coding genes were annotated. Its compact genome size could be credited with multiple features such as very low repeat content (5.1%) due to the lack of proliferation of transposable elements, high gene density (189.1/Mb), more intronless genes (20.3%) and low microsatellite density (0.63%)., (© 2024. The Author(s).)

Inflammation and loss of articular cartilage are considered the major cause of temporomandibular joint osteoarthritis (TMJOA), a painful condition of the temporomandibular joint (TMJ). To determine the cause of TMJ osteoarthritis in these patients, synovial fluid of TMJOA patients was compared prior to and after hyaluronic lavage, revealing substantially elevated levels of interleukin (IL) 1β, reactive oxidative stress (ROS), and an overload of Fe 3+ and Fe 2+ prior to lavage, indicative of ferroptosis as a mode of chondrocyte cell death. To ask whether prolonged inflammatory conditions resulted in ferroptosis-like transformation in vitro, we subjected TMJ chondrocytes to IL-1β treatment, resulting in a shift in messenger RNA sequencing gene ontologies related to iron homeostasis and oxidative stress-related cell death. Exposure to rat unilateral anterior crossbite conditions resulted in reduced COL2A1 expression, fewer chondrocytes, glutathione peroxidase 4 (GPX4) downregulation, and 4-hydroxynonenal (4-HNE) upregulation, an effect that was reversed after intra-articular injections of the ferroptosis inhibitor ferrostatin 1 (Fer-1). Our study demonstrated that ferroptosis conditions affected mitochondrial structure and function, while the inhibitor Fer-1 restored mitochondrial structure and the inhibition of hypoxia-inducible factor 1α (HIF-1α) or the transferrin receptor 1 (TFRC) rescued IL-1β-induced loss of mitochondrial membrane potential. Inhibition of HIF-1α downregulated IL-1β-induced TFRC expression, while inhibition of TFRC did not downregulate IL-1β-induced HIF-1α expression in chondrocytes. Moreover, inhibition of HIF-1α or TFRC downregulated the IL-1β-induced MMP13 expression in chondrocytes, while inhibition of HIF-1α or TFRC rescued IL-1β-inhibited COL2A1 expression in chondrocytes. Furthermore, upregulation of TFRC promoted Fe 2+ entry into chondrocytes, inducing the Fenton reaction and lipid peroxidation, which in turn caused ferroptosis, a disruption in chondrocyte functions, and an exacerbation of condylar cartilage degeneration. Together, these findings illustrate the far-reaching effects of chondrocyte ferroptosis in TMJOA as a mechanism causing chondrocyte death through iron overload, oxidative stress, and articular cartilage degeneration and a potential major cause of TMJOA., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Importance: Suicide rates in the US increased by 35.6% from 2001 to 2021. Given that most individuals die on their first attempt, earlier detection and intervention are crucial. Understanding modifiable risk factors is key to effective prevention strategies., Objective: To identify distinct suicide profiles or classes, associated signs of suicidal intent, and patterns of modifiable risks for targeted prevention efforts., Design, Setting, and Participants: This cross-sectional study used data from the 2003-2020 National Violent Death Reporting System Restricted Access Database for 306 800 suicide decedents. Statistical analysis was performed from July 2022 to June 2023., Exposures: Suicide decedent profiles were determined using latent class analyses of available data on suicide circumstances, toxicology, and methods., Main Outcomes and Measures: Disclosure of recent intent, suicide note presence, and known psychotropic usage., Results: Among 306 800 suicide decedents (mean [SD] age, 46.3 [18.4] years; 239 627 males [78.1%] and 67 108 females [21.9%]), 5 profiles or classes were identified. The largest class, class 4 (97 175 [31.7%]), predominantly faced physical health challenges, followed by polysubstance problems in class 5 (58 803 [19.2%]), and crisis, alcohol-related, and intimate partner problems in class 3 (55 367 [18.0%]), mental health problems (class 2, 53 928 [17.6%]), and comorbid mental health and substance use disorders (class 1, 41 527 [13.5%]). Class 4 had the lowest rates of disclosing suicidal intent (13 952 [14.4%]) and leaving a suicide note (24 351 [25.1%]). Adjusting for covariates, compared with class 1, class 4 had the highest odds of not disclosing suicide intent (odds ratio [OR], 2.58; 95% CI, 2.51-2.66) and not leaving a suicide note (OR, 1.45; 95% CI, 1.41-1.49). Class 4 also had the lowest rates of all known psychiatric illnesses and psychotropic medications among all suicide profiles. Class 4 had more older adults (23 794 were aged 55-70 years [24.5%]; 20 100 aged ≥71 years [20.7%]), veterans (22 220 [22.9%]), widows (8633 [8.9%]), individuals with less than high school education (15 690 [16.1%]), and rural residents (23 966 [24.7%])., Conclusions and Relevance: This study identified 5 distinct suicide profiles, highlighting a need for tailored prevention strategies. Improving the detection and treatment of coexisting mental health conditions, substance and alcohol use disorders, and physical illnesses is paramount. The implementation of means restriction strategies plays a vital role in reducing suicide risks across most of the profiles, reinforcing the need for a multifaceted approach to suicide prevention.

Social factors like family background, education level, financial status, and stress can impact public health outcomes, such as suicidal ideation. However, the analysis of social factors for suicide prevention has been limited by the lack of up-to-date suicide reporting data, variations in reporting practices, and small sample sizes. In this study, we analyzed 172,629 suicide incidents from 2014 to 2020 utilizing the National Violent Death Reporting System Restricted Access Database (NVDRS-RAD). Logistic regression models were developed to examine the relationships between demographics and suicide-related circumstances. Trends over time were assessed, and Latent Dirichlet Allocation (LDA) was used to identify common suicide-related social factors. Mental health, interpersonal relationships, mental health treatment and disclosure, and school/work-related stressors were identified as the main themes of suicide-related social factors. This study also identified systemic disparities across various population groups, particularly concerning Black individuals, young people aged under 24, healthcare practitioners, and those with limited education backgrounds, which shed light on potential directions for demographic-specific suicidal interventions.

Objective: We developed and externally validated a machine-learning model to predict postpartum depression (PPD) using data from electronic health records (EHRs). Effort is under way to implement the PPD prediction model within the EHR system for clinical decision support. We describe the pre-implementation evaluation process that considered model performance, fairness, and clinical appropriateness., Materials and Methods: We used EHR data from an academic medical center (AMC) and a clinical research network database from 2014 to 2020 to evaluate the predictive performance and net benefit of the PPD risk model. We used area under the curve and sensitivity as predictive performance and conducted a decision curve analysis. In assessing model fairness, we employed metrics such as disparate impact, equal opportunity, and predictive parity with the White race being the privileged value. The model was also reviewed by multidisciplinary experts for clinical appropriateness. Lastly, we debiased the model by comparing 5 different debiasing approaches of fairness through blindness and reweighing., Results: We determined the classification threshold through a performance evaluation that prioritized sensitivity and decision curve analysis. The baseline PPD model exhibited some unfairness in the AMC data but had a fair performance in the clinical research network data. We revised the model by fairness through blindness, a debiasing approach that yielded the best overall performance and fairness, while considering clinical appropriateness suggested by the expert reviewers., Discussion and Conclusion: The findings emphasize the need for a thorough evaluation of intervention-specific models, considering predictive performance, fairness, and appropriateness before clinical implementation., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: To develop a natural language processing (NLP) package to extract social determinants of health (SDoH) from clinical narratives, examine the bias among race and gender groups, test the generalizability of extracting SDoH for different disease groups, and examine population-level extraction ratio., Methods: We developed SDoH corpora using clinical notes identified at the University of Florida (UF) Health. We systematically compared 7 transformer-based large language models (LLMs) and developed an open-source package - SODA (i.e., SOcial DeterminAnts) to facilitate SDoH extraction from clinical narratives. We examined the performance and potential bias of SODA for different race and gender groups, tested the generalizability of SODA using two disease domains including cancer and opioid use, and explored strategies for improvement. We applied SODA to extract 19 categories of SDoH from the breast (n = 7,971), lung (n = 11,804), and colorectal cancer (n = 6,240) cohorts to assess patient-level extraction ratio and examine the differences among race and gender groups., Results: We developed an SDoH corpus using 629 clinical notes of cancer patients with annotations of 13,193 SDoH concepts/attributes from 19 categories of SDoH, and another cross-disease validation corpus using 200 notes from opioid use patients with 4,342 SDoH concepts/attributes. We compared 7 transformer models and the GatorTron model achieved the best mean average strict/lenient F1 scores of 0.9122 and 0.9367 for SDoH concept extraction and 0.9584 and 0.9593 for linking attributes to SDoH concepts. There is a small performance gap (∼4%) between Males and Females, but a large performance gap (>16 %) among race groups. The performance dropped when we applied the cancer SDoH model to the opioid cohort; fine-tuning using a smaller opioid SDoH corpus improved the performance. The extraction ratio varied in the three cancer cohorts, in which 10 SDoH could be extracted from over 70 % of cancer patients, but 9 SDoH could be extracted from less than 70 % of cancer patients. Individuals from the White and Black groups have a higher extraction ratio than other minority race groups., Conclusions: Our SODA package achieved good performance in extracting 19 categories of SDoH from clinical narratives. The SODA package with pre-trained transformer models is available at https://github.com/uf-hobi-informatics-lab/SODA&#95;Docker., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Aims: Atrial fibrillation (AF) symptom relief is a primary indication for catheter ablation, but AF symptom resolution is not well characterized. The study objective was to describe AF symptom documentation in electronic health records (EHRs) pre- and post-ablation and identify correlates of post-ablation symptoms., Methods and Results: We conducted a retrospective cohort study using EHRs of patients with AF (n = 1293), undergoing ablation in a large, urban health system from 2010 to 2020. We extracted symptom data from clinical notes using a natural language processing algorithm (F score: 0.81). We used Cochran's Q tests with post-hoc McNemar's tests to determine differences in symptom prevalence pre- and post-ablation. We used logistic regression models to estimate the adjusted odds of symptom resolution by personal or clinical characteristics at 6 and 12 months post-ablation. In fully adjusted models, at 12 months post-ablation patients, patients with heart failure had significantly lower odds of dyspnoea resolution [odds ratio (OR) 0.38, 95% confidence interval (CI) 0.25-0.57], oedema resolution (OR 0.37, 95% CI 0.25-0.56), and fatigue resolution (OR 0.54, 95% CI 0.34-0.85), but higher odds of palpitations resolution (OR 1.90, 95% CI 1.25-2.89) compared with those without heart failure. Age 65 and older, female sex, Black or African American race, smoking history, and antiarrhythmic use were also associated with lower odds of resolution of specific symptoms at 6 and 12 months., Conclusion: The post-ablation symptom patterns are heterogeneous. Findings warrant confirmation with larger, more representative data sets, which may be informative for patients whose primary goal for undergoing an ablation is symptom relief., Competing Interests: Conflict of interest: M.R.T. and J.P. are affiliated with Iris OB Health LLC (equity). M.R.T. is affiliated with Boston Scientific Corp (consulting). The remaining authors have no disclosures., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Pregnant women with abnormal liver function tests (LFTs) require proper evaluation and timely management to reduce maternal and fetal morbidity and mortality., Objective: The present study was done with the objective of determining feto-maternal outcomes in antenatal women with abnormal LFTs and comparing them with antenatal women having normal liver function. The prevalence and possible causes of derangements in LFT were also identified., Method: Pregnant women referred to an antenatal clinic for several reasons pertaining to abnormal liver functions, and those admitted to the labor room for delivery with abnormal LFTs were included in the study. The pregnant women with abnormal LFT were studied prospectively, and they were compared with pregnant women having normal LFT. The fetal and maternal outcomes were also noted., Results:  The pregnant women attending the antenatal clinic with a history of pruritus, abdominal pain, jaundice, nausea/vomiting, hypertension ascites, etc. and delivered at our facility were evaluated. One hundred and eight women had abnormal LFT defined by criteria laid down in material and methods. Eighty-seven women with normal LFT were taken for comparison. In the abnormal LFT, the main cause was intrahepatic cholestasis of pregnancy (IHCP). There were 6 (5.5%) maternal deaths in this group and none in the normal LFTs. There were 6 (5.6%) fetal deaths and 4 (4.6%) in the other group (p-value=1). The prevalence of abnormal LFT was 9.11% throughout pregnancy. Increased bilirubin and alkaline phosphatase (ALP) were significantly correlated with maternal mortality, while gestational age at birth, presence of meconium, appearance, pulse, grimace, activity, and respiration (APGAR) score, maternal mortality, and raised alkaline phosphatase level were found to be significantly associated with fetal mortality., Conclusion: Patients with abnormal LFT were significantly associated with maternal morbidity and mortality. However, fetal outcomes in patients with abnormal and normal LFT were similar. Hyperbilirubinemia and raised alanine aminotransferase (ALT) were significant predictors of maternal mortality., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2024, Pathak et al.)

Objective: Personal and family history of suicidal thoughts and behaviors (PSH and FSH, respectively) are significant risk factors associated with future suicide events. These are often captured in narrative clinical notes in electronic health records (EHRs). Collaboratively, Weill Cornell Medicine (WCM), Northwestern Medicine (NM), and the University of Florida (UF) developed and validated deep learning (DL)-based natural language processing (NLP) tools to detect PSH and FSH from such notes. The tool's performance was further benchmarked against a method relying exclusively on ICD-9/10 diagnosis codes., Materials and Methods: We developed DL-based NLP tools utilizing pre-trained transformer models Bio&#95;ClinicalBERT and GatorTron, and compared them with expert-informed, rule-based methods. The tools were initially developed and validated using manually annotated clinical notes at WCM. Their portability and performance were further evaluated using clinical notes at NM and UF., Results: The DL tools outperformed the rule-based NLP tool in identifying PSH and FHS. For detecting PSH, the rule-based system obtained an F1-score of 0.75 ± 0.07, while the Bio&#95;ClinicalBERT and GatorTron DL tools scored 0.83 ± 0.09 and 0.84 ± 0.07, respectively. For detecting FSH, the rule-based NLP tool's F1-score was 0.69 ± 0.11, compared to 0.89 ± 0.10 for Bio&#95;ClinicalBERT and 0.92 ± 0.07 for GatorTron. For the gold standard corpora across the three sites, only 2.2% (WCM), 9.3% (NM), and 7.8% (UF) of patients reported to have an ICD-9/10 diagnosis code for suicidal thoughts and behaviors prior to the clinical notes report date. The best performing GatorTron DL tool identified 93.0% (WCM), 80.4% (NM), and 89.0% (UF) of patients with documented PSH, and 85.0%(WCM), 89.5%(NM), and 100%(UF) of patients with documented FSH in their notes., Discussion: While PSH and FSH are significant risk factors for future suicide events, little effort has been made previously to identify individuals with these history. To address this, we developed a transformer based DL method and compared with conventional rule-based NLP approach. The varying effectiveness of the rule-based tools across sites suggests a need for improvement in its dictionary-based approach. In contrast, the performances of the DL tools were higher and comparable across sites. Furthermore, DL tools were fine-tuned using only small number of annotated notes at each site, underscores its greater adaptability to local documentation practices and lexical variations., Conclusion: Variations in local documentation practices across health care systems pose challenges to rule-based NLP tools. In contrast, the developed DL tools can effectively extract PSH and FSH information from unstructured clinical notes. These tools will provide clinicians with crucial information for assessing and treating patients at elevated risk for suicide who are rarely been diagnosed., Competing Interests: Competing interests All authors declare no financial or non-financial competing interests

Objectives: To determine if different formats for conveying machine learning (ML)-derived postpartum depression risks impact patient classification of recommended actions (primary outcome) and intention to seek care, perceived risk, trust, and preferences (secondary outcomes)., Materials and Methods: We recruited English-speaking females of childbearing age (18-45 years) using an online survey platform. We created 2 exposure variables (presentation format and risk severity), each with 4 levels, manipulated within-subject. Presentation formats consisted of text only, numeric only, gradient number line, and segmented number line. For each format viewed, participants answered questions regarding each outcome., Results: Five hundred four participants (mean age 31 years) completed the survey. For the risk classification question, performance was high (93%) with no significant differences between presentation formats. There were main effects of risk level (all P < .001) such that participants perceived higher risk, were more likely to agree to treatment, and more trusting in their obstetrics team as the risk level increased, but we found inconsistencies in which presentation format corresponded to the highest perceived risk, trust, or behavioral intention. The gradient number line was the most preferred format (43%)., Discussion and Conclusion: All formats resulted high accuracy related to the classification outcome (primary), but there were nuanced differences in risk perceptions, behavioral intentions, and trust. Investigators should choose health data visualizations based on the primary goal they want lay audiences to accomplish with the ML risk score., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Obtaining reliable data on patient mortality is a critical challenge facing observational researchers seeking to conduct studies using real-world data. As these analyses are conducted more broadly using newly-available sources of real-world evidence, missing data can serve as a rate-limiting factor. We conducted a comparison of mortality data sources from different stakeholder perspectives - academic medical center (AMC) informatics service providers, AMC research coordinators, industry analytics professionals, and academics - to understand the strengths and limitations of differing mortality data sources: locally generated data from sites conducting research, data provided by governmental sources, and commercially available data sets. Researchers seeking to conduct observational studies using extant data should consider these factors in sourcing outcomes data for their populations of interest., (©2023 AMIA - All rights reserved.)

Introduction: Prior literature establishes noteworthy relationships between suicidal symptoms and substance use disorders (SUDs), particularly opioid use disorder (OUD). However, engagement with health care services among this vulnerable population remains underinvestigated. This study sought to examine patterns of health care use, identify risk factors in seeking treatment, and assess associations between outpatient service use and emergency department (ED) visits., Methods: Using electronic health records (EHRs) derived from five health systems across New York City, the study selected 7881 adults with suicidal symptoms (including suicidal ideation, suicide attempt, or self-harm) and SUDs between 2010 and 2019. To examine the association between SUDs (including OUD) and all-cause service use (outpatient, inpatient, and ED), we performed quasi-Poisson regressions adjusted for age, gender, and chronic disease burden, and we estimated the relative risks (RR) of associated factors. Next, the study evaluated cause-specific utilization within each resource category (SUD-related, suicide-related, and other-psychiatric) and compared them using Mann-Whitney U tests. Finally, we used adjusted quasi-Poisson regression models to analyze the association between outpatient and ED utilization among different risk groups., Results: Among patients with suicidal symptoms and SUD diagnoses, relative to other SUDs, a diagnosis of OUD was associated with higher all-cause outpatient visits (RR: 1.22), ED visits (RR: 1.54), and inpatient hospitalizations (RR: 1.67) (ps < 0.001). Men had a lower risk of having outpatient visits (RR: 0.80) and inpatient hospitalizations (RR: 0.90), and older age protected against ED visits (RR range: 0.59-0.69) (ps < 0.001). OUD was associated with increased SUD-related encounters across all settings, and increased suicide-related ED visits and inpatient hospitalizations (p < 0.001). Individuals with more mental health outpatient visits were less likely to have suicide-related ED visits (RR: 0.86, p < 0.01), however this association was not found among younger and male patients with OUD. Although few OUD patients received medications for OUD (MOUD) treatment (9.9 %), methadone composed the majority of MOUD prescriptions (77.7 %), of which over 70 % were prescribed during an ED encounter., Conclusions: This study reinforces the importance of tailoring SUD and suicide risk interventions to different age groups and types of SUDs, and highlights missed opportunities for deploying screening and prevention resources among the male and OUD populations. Redressing underutilization of MOUD remains a priority to reduce acute health outcomes among younger patients with OUD., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Importance: To our knowledge, IL-10-KO mice have not previously been used to investigate the interactions of host, microbiota, and broccoli, broccoli sprouts, or broccoli bioactives in resolving symptoms of CD. We showed that a diet containing 10% raw broccoli sprouts increased the plasma concentration of the anti-inflammatory compound sulforaphane and protected mice to varying degrees against disease symptoms, including weight loss or stagnation, fecal blood, and diarrhea. Younger mice responded more strongly to the diet, further reducing symptoms, as well as increased gut bacterial richness, increased bacterial community similarity to each other, and more location-specific communities than older mice on the diet intervention. Crohn's disease disrupts the lives of patients and requires people to alter dietary and lifestyle habits to manage symptoms. The current medical treatment is expensive with significant side effects, and a dietary intervention represents an affordable, accessible, and simple strategy to reduce the burden of symptoms., Competing Interests: The authors declare no conflict of interest.

Importance: Social determinants of health (SDOH) influence child health. However, most previous studies have used individual, small-set, or cherry-picked SDOH variables without examining unbiased computed SDOH patterns from high-dimensional SDOH factors to investigate associations with child mental health, cognition, and physical health., Objective: To identify SDOH patterns and estimate their associations with children's mental, cognitive, and physical developmental outcomes., Design, Setting, and Participants: This population-based cohort study included children aged 9 to 10 years at baseline and their caregivers enrolled in the Adolescent Brain Cognitive Development (ABCD) Study between 2016 and 2021. The ABCD Study includes 21 sites across 17 states., Exposures: Eighty-four neighborhood-level, geocoded variables spanning 7 domains of SDOH, including bias, education, physical and health infrastructure, natural environment, socioeconomic status, social context, and crime and drugs, were studied. Hierarchical agglomerative clustering was used to identify SDOH patterns., Main Outcomes and Measures: Associations of SDOH and child mental health (internalizing and externalizing behaviors) and suicidal behaviors, cognitive function (performance, reading skills), and physical health (body mass index, exercise, sleep disorder) were estimated using mixed-effects linear and logistic regression models., Results: Among 10 504 children (baseline median [SD] age, 9.9 [0.6] years; 5510 boys [52.5%] and 4994 girls [47.5%]; 229 Asian [2.2%], 1468 Black [14.0%], 2128 Hispanic [20.3%], 5565 White [53.0%], and 1108 multiracial [10.5%]), 4 SDOH patterns were identified: pattern 1, affluence (4078 children [38.8%]); pattern 2, high-stigma environment (2661 children [25.3%]); pattern 3, high socioeconomic deprivation (2653 children [25.3%]); and pattern 4, high crime and drug sales, low education, and high population density (1112 children [10.6%]). The SDOH patterns were distinctly associated with child health outcomes. Children exposed to socioeconomic deprivation (SDOH pattern 3) showed the worst health profiles, manifesting more internalizing (β = 0.75; 95% CI, 0.14-1.37) and externalizing (β = 1.43; 95% CI, 0.83-2.02) mental health problems, lower cognitive performance, and adverse physical health., Conclusions: This study shows that an unbiased quantitative analysis of multidimensional SDOH can permit the determination of how SDOH patterns are associated with child developmental outcomes. Children exposed to socioeconomic deprivation showed the worst outcomes relative to other SDOH categories. These findings suggest the need to determine whether improvement in socioeconomic conditions can enhance child developmental outcomes.

Objective: Evidence suggests that high-quality health education and effective communication within the framework of social support hold significant potential in preventing postpartum depression. Yet, developing trustworthy and engaging health education and communication materials requires extensive expertise and substantial resources. In light of this, we propose an innovative approach that involves leveraging natural language processing (NLP) to classify publicly accessible lay articles based on their relevance and subject matter to pregnancy and mental health., Materials and Methods: We manually reviewed online lay articles from credible and medically validated sources to create a gold standard corpus. This manual review process categorized the articles based on their pertinence to pregnancy and related subtopics. To streamline and expand the classification procedure for relevance and topics, we employed advanced NLP models such as Random Forest, Bidirectional Encoder Representations from Transformers (BERT), and Generative Pre-trained Transformer model (gpt-3.5-turbo)., Results: The gold standard corpus included 392 pregnancy-related articles. Our manual review process categorized the reading materials according to lifestyle factors associated with postpartum depression: diet, exercise, mental health , and health literacy . A BERT-based model performed best (F1 = 0.974) in an end-to-end classification of relevance and topics. In a two-step approach, given articles already classified as pregnancy-related, gpt-3.5-turbo performed best (F1 = 0.972) in classifying the above topics., Discussion: Utilizing NLP, we can guide patients to high-quality lay reading materials as cost-effective, readily available health education and communication sources. This approach allows us to scale the information delivery specifically to individuals, enhancing the relevance and impact of the materials provided., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Patra, Sun, Cheng, Kumar, Altammami, Liu, Joly, Jedlicka, Delgado, Pathak, Peng and Zhang.)

Objective: Geography may influence the relationships of predictors for suicidal ideation (SI) and suicide attempts (SA) in children and youth., Method: This is a nationwide retrospective cohort study of 124,424 individuals less than 25 years of age using commercial claims data (2011-2015) from the Health Care Cost Institute. Outcomes were time to SI or SA within 3 months after the indexed mental health or substance use disorder (MH/SUD) outpatient visit. Predictors included sociodemographic and clinical characteristics up to 3 years before the index event., Results: At each follow-up time period, rates of SI and SA varied by the US geographic division ( p < .001), and the Mountain Division consistently had the highest rates for both SI and SA (5.44%-10.26% for SI; 0.70%-2.82% for SA). Having MH emergency department (ED) visits in the past year increased the risk of SI by 28% to 65% for individuals residing in the New England, Mid-Atlantic, East North Central, West North Central, and East South Central Divisions. The main effects of geographic divisions were significant for SA (p<0.001). Risk of SA was lower in New England, Mid-Atlantic, South Atlantic, and Pacific (hazard ratios = 0.57, 0.51, 0.67, and 0.79, respectively) and higher in the Mountain Division (hazard ratio = 1.46)., Conclusion: To understand the underlying mechanisms driving the high prevalence of SI and SA in the Mountain Division and the elevated risk of SI after having MH ED visits, future research examining regional differences in risks for SI and SA should include indicators of access to MH ED care and other social determinants of health., Competing Interests: Disclosure: Dr. Pathak is the Cofounder of Iris OB Health Inc., New York and has equity ownership. Drs. Xi, Banerjee, Zima, Alexopoulos, Olfson, and Xiao have reported no biomedical financial interests or potential conflicts of interest.

Background: Depression and anxiety are common and highly comorbid, and their comorbidity is associated with poorer outcomes posing clinical and public health concerns. We evaluated the polygenic contribution to comorbid depression and anxiety, and to each in isolation., Methods: Diagnostic codes were extracted from electronic health records for four biobanks [ N = 177 865 including 138 632 European (77.9%), 25 612 African (14.4%), and 13 621 Hispanic (7.7%) ancestry participants]. The outcome was a four-level variable representing the depression/anxiety diagnosis group: neither, depression-only, anxiety-only, and comorbid. Multinomial regression was used to test for association of depression and anxiety polygenic risk scores (PRSs) with the outcome while adjusting for principal components of ancestry., Results: In total, 132 960 patients had neither diagnosis (74.8%), 16 092 depression-only (9.0%), 13 098 anxiety-only (7.4%), and 16 584 comorbid (9.3%). In the European meta-analysis across biobanks, both PRSs were higher in each diagnosis group compared to controls. Notably, depression-PRS (OR 1.20 per s.d. increase in PRS; 95% CI 1.18-1.23) and anxiety-PRS (OR 1.07; 95% CI 1.05-1.09) had the largest effect when the comorbid group was compared with controls. Furthermore, the depression-PRS was significantly higher in the comorbid group than the depression-only group (OR 1.09; 95% CI 1.06-1.12) and the anxiety-only group (OR 1.15; 95% CI 1.11-1.19) and was significantly higher in the depression-only group than the anxiety-only group (OR 1.06; 95% CI 1.02-1.09), showing a genetic risk gradient across the conditions and the comorbidity., Conclusions: This study suggests that depression and anxiety have partially independent genetic liabilities and the genetic vulnerabilities to depression and anxiety make distinct contributions to comorbid depression and anxiety.

Variation in availability, format, and standardization of patient attributes across health care organizations impacts patient-matching performance. We report on the changing nature of patient-matching features available from 2010-2020 across diverse care settings. We asked 38 health care provider organizations about their current patient attribute data-collection practices. All sites collected name, date of birth (DOB), address, and phone number. Name, DOB, current address, social security number (SSN), sex, and phone number were most commonly used for cross-provider patient matching. Electronic health record queries for a subset of 20 participating sites revealed that DOB, first name, last name, city, and postal codes were highly available (>90%) across health care organizations and time. SSN declined slightly in the last years of the study period. Birth sex, gender identity, language, country full name, country abbreviation, health insurance number, ethnicity, cell phone number, email address, and weight increased over 50% from 2010 to 2020. Understanding the wide variation in available patient attributes across care settings in the United States can guide selection and standardization efforts for improved patient matching in the United States., Competing Interests: Conflicts of interest Please see ICMJE form(s) for author conflicts of interest. These have been provided as supplementary materials., (Published by Oxford University Press on behalf of Project HOPE - The People-To-People Health Foundation, Inc. 2023.)

Cyanobacteria are challenged by daily fluctuations of light intensities and photoperiod in their natural habitats, which affect the physiology and fitness of cyanobacteria. Circadian rhythms (CRs), an important endogenous process found in all organisms including cyanobacteria, control their physiological activities and helps in coping with 24-h light/dark (LD) cycle. In cyanobacteria, physiological responses under rhythmic ultraviolet radiation (UVR) are poorly studied. Therefore, we studied the changes in photosynthetic pigments, and physiological parameters of Synechocystis sp. PCC 6803 under UVR and photosynthetically active radiation (PAR) of light/dark (LD) oscillations having the combinations of 0, 4:20, 8:16, 12:12, 16:8, 20:4, and 24:24 h. The LD 16:8 enhanced the growth, pigments, proteins, photosynthetic efficiency, and physiology of Synechocystis sp. PCC6803. Continuous light (LL 24) of UVR and PAR exerted negative impact on the photosynthetic pigments, and chlorophyll fluorescence. Significant increase in reactive oxygen species (ROS) resulted in loss of plasma membrane integrity followed by decreased viability of cells. The dark phase played a significant role in Synechocystis to withstand the LL 24 under PAR and UVR. This study offers detailed understanding of the physiological responses of the cyanobacterium to changing light environment., (© 2023. The Author(s), under exclusive licence to European Photochemistry Association, European Society for Photobiology.)

The enhanced availability of functional fibroblasts from precious tissue samples requires an ideal cell-culture system. Therefore, this study was designed to investigate the performance of caprine adult fibroblast cells (cadFibroblast) when cultivated in different culture media. The cadFibroblast cell lines from adult Barbari (Capra hircus) bucks were established and the effect of different media viz. DMEM/F-12 [with low-glucose (5.5 mM; DL) and high-glucose (30 mM; DH)], α-MEM [with low-glucose (5.5 mM; ML) and with high-glucose (30 mM; MH)], and fibroblast growth medium (FGM) were evaluated. Cells were then compared for growth characteristics and in-vitro dynamics through cellular morphology, proliferation, population-doubling time, double-immunocytochemistry, colony-forming units, wound healing, transwell migration, and differential expression of fibroblast-specific markers (FSP-1 and vimentin). The results of immunocytochemistry, transwell migration/invasion, and wound healing assays showed the superiority of DH over DL and other media tested. Whereas, similar effects of glucose supplementation and expression of FSP-1 were not observed in α-MEM. Transwell migration was significantly (p < 0.05) lower in FGM compared with other media tested. Overall, our results illustrate the media-dependent deviation in in-vitro dynamics and culture characteristics of cadFibroblasts that may be useful to develop strategies to cultivate these cells efficiently for research and downstream applications., (© 2023. Springer Nature Limited.)

Objectives: To describe and compare the association between suicidality and subsequent readmission for patients hospitalized for eating disorder treatment, within 2 years of discharge, at two large academic medical centers in two different countries., Methods: Over an 8-year study window from January 2009 to March 2017, we identified all inpatient eating disorder admissions at Weill Cornell Medicine, New York, USA (WCM) and South London and Maudsley Foundation NHS Trust, London, UK (SLaM). To establish each patient's-suicidality profile, we applied two natural language processing (NLP) algorithms, independently developed at the two institutions, and detected suicidality in clinical notes documented in the first week of admission. We calculated the odds ratios (OR) for any subsequent readmission within 2 years postdischarge and determined whether this was to another eating disorder unit, other psychiatric unit, a general medical hospital admission or emergency room attendance., Results: We identified 1126 and 420 eating disorder inpatient admissions at WCM and SLaM, respectively. In the WCM cohort, evidence of above average suicidality during the first week of admission was significantly associated with an increased risk of noneating disorder-related psychiatric readmission (OR 3.48 95% CI = 2.03-5.99, p-value < .001), but a similar pattern was not observed in the SLaM cohort (OR 1.34, 95% CI = 0.75-2.37, p = .32), there was no significant increase in risk of admission. In both cohorts, personality disorder increased the risk of any psychiatric readmission within 2 years., Discussion: Patterns of increased risk of psychiatric readmission from above average suicidality detected via NLP during inpatient eating disorder admissions differed in our two patient cohorts. However, comorbid diagnoses such as personality disorder increased the risk of any psychiatric readmission across both cohorts., Public Significance: Suicidality amongst is eating disorders is an extremely common presentation and it is important we further our understanding of identifying those most at risk. This research also provides a novel study design, comparing two NLP algorithms on electronic health record data based in the United States and United Kingdom on eating disorder inpatients. Studies researching both UK and US mental health patients are sparse therefore this study provides novel data., (© 2023 The Authors. International Journal of Eating Disorders published by Wiley Periodicals LLC.)

Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of disease progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD)., Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000 and 2020. The progress notes no later than the first MCI diagnosis were used for the prediction. We first preprocessed the notes by deidentification, cleaning and splitting into sections, and then pre-trained a BERT model for AD (named AD-BERT) based on the publicly available Bio+Clinical BERT on the preprocessed notes. All sections of a patient were embedded into a vector representation by AD-BERT and then combined by global MaxPooling and a fully connected network to compute the probability of MCI-to-AD progression. For validation, we conducted a similar set of experiments on 2563 MCI patients identified at Weill Cornell Medicine (WCM) during the same timeframe., Results: Compared with the 7 baseline models, the AD-BERT model achieved the best performance on both datasets, with Area Under receiver operating characteristic Curve (AUC) of 0.849 and F1 score of 0.440 on NMEDW dataset, and AUC of 0.883 and F1 score of 0.680 on WCM dataset., Conclusion: The use of EHRs for AD-related research is promising, and AD-BERT shows superior predictive performance in modeling MCI-to-AD progression prediction. Our study demonstrates the utility of pre-trained language models and clinical notes in predicting MCI-to-AD progression, which could have important implications for improving early detection and intervention for AD., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objectives: Suicide presents a major public health challenge worldwide, affecting people across the lifespan. While previous studies revealed strong associations between Social Determinants of Health (SDoH) and suicide deaths, existing evidence is limited by the reliance on structured data. To resolve this, we aim to adapt a suicide-specific SDoH ontology (Suicide-SDoHO) and use natural language processing (NLP) to effectively identify individual-level SDoH-related social risks from death investigation narratives., Materials and Methods: We used the latest National Violent Death Report System (NVDRS), which contains 267 804 victim suicide data from 2003 to 2019. After adapting the Suicide-SDoHO, we developed a transformer-based model to identify SDoH-related circumstances and crises in death investigation narratives. We applied our model retrospectively to annotate narratives whose crisis variables were not coded in NVDRS. The crisis rates were calculated as the percentage of the group's total suicide population with the crisis present., Results: The Suicide-SDoHO contains 57 fine-grained circumstances in a hierarchical structure. Our classifier achieves AUCs of 0.966 and 0.942 for classifying circumstances and crises, respectively. Through the crisis trend analysis, we observed that not everyone is equally affected by SDoH-related social risks. For the economic stability crisis, our result showed a significant increase in crisis rate in 2007-2009, parallel with the Great Recession., Conclusions: This is the first study curating a Suicide-SDoHO using death investigation narratives. We showcased that our model can effectively classify SDoH-related social risks through NLP approaches. We hope our study will facilitate the understanding of suicide crises and inform effective prevention strategies., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

The development of nanotechnology, in particular metal oxide nanoparticles, has captured immense scientific attention in the global arena due to their unique properties leading to their unique diverse applications. But the use of toxic precursors and high operational cost make existing methodologies inefficient for synthesising metal oxide nanoparticles (MONPs). Biogenic synthesis of MONPs has been hailed as a more sustainable approach for the synthesis of NPs due to its alignment with the principles of green chemistry. Microorganisms (bacteria, yeast, algae), animal sources (silk, fur, etc.), and plants are effective, low-cost, and eco-friendly means of synthesizing MONPs since they possess a high bio-reduction abilities to produce NPs of various shapes and sizes. The current review encompasses recent advancements in the field of plant-mediated MONP synthesis and characterisation. The detailed evaluation of various synthesis processes and parameters, key influencing factors affecting the synthesis efficiency and product morphology, practical applications with insight into the associated limitations and challenges presents a valuable database that will be helpful in developing alternative prospects and potential engineering applications., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents ( n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians, developers, healthcare systems, and the government responsible for harm. Most reported it was "very important" for them to understand AI output. More previously pregnant respondents reported being told AI played a small role in mental healthcare was "very important" versus those not previously pregnant ( P = .03). We conclude that protections against harm, transparency around data use, preservation of the patient-clinician relationship, and patient comprehension of AI predictions may facilitate trust in AI-based technologies for mental healthcare among women., Competing Interests: MRT, AH, and JP are affiliated with and have equity in Iris OB Health. The remaining authors have no competing interests to declare., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Crohn's Disease (CD) is a presentation of Inflammatory Bowel Disease (IBD) that manifests in childhood and adolescence, and involves chronic and severe enterocolitis, immune and gut microbiome dysregulation, and other complications. Diet and gut-microbiota-produced metabolites are sources of anti-inflammatories which could ameliorate symptoms. However, questions remain on how IBD influences biogeographic patterns of microbial location and function in the gut, how early life transitional gut communities are affected by IBD and diet interventions, and how disruption to biogeography alters disease mediation by diet components or microbial metabolites. Many studies on diet and IBD use a chemically induced ulcerative colitis model, despite the availability of an immune-modulated CD model. Interleukin-10-knockout (IL-10-KO) mice on a C57BL/6 background, beginning at age 4 or 7 weeks, were fed a control diet or one containing 10% (w/w) raw broccoli sprouts, which was high in the sprout-sourced anti-inflammatory sulforaphane. Diets began 7 days prior to, and for 2 weeks after inoculation with Helicobacter hepaticus , which triggers Crohn's-like symptoms in these immune-impaired mice. The broccoli sprout diet increased sulforaphane in plasma; decreased weight stagnation, fecal blood, and diarrhea associated; and increased microbiota richness in the gut, especially in younger mice. Sprout diets resulted in some anatomically specific bacteria in younger mice, and reduced the prevalence and abundance of pathobiont bacteria which trigger inflammation in the IL-10-KO mouse, for example; Escherichia coli and Helicobacter . Overall, the IL-10-KO mouse model is responsive to a raw broccoli sprout diet and represents an opportunity for more diet-host-microbiome research.

Burkitt lymphoma (BL) is a poorly differentiated, aggressive form of B-cell non-Hodgkin's lymphoma. The clinical presentation of this disease is varied and may be nodal, extranodal, or both. BL of the breast, either primary or secondary, with bilateral breast involvement, is extremely rare. Herein, we present a case of BL in a 27-year-old male with unusual bilateral breast involvement., Competing Interests: There are no conflicts of interest., (Copyright: © 2023 Indian Journal of Nuclear Medicine.)

Opsoclonus myoclonus ataxia syndrome (OMAS) is a rare inflammatory neurological disorder characterized by ocular, motor, behavioral, language, and sleep disturbances. It usually affects infants and young children but may affect adults. A 28-year-old male was brought to our emergency ward with complaints of involuntary spontaneous eye movements and jerky movements of limbs with imbalance while walking. He had a history of short febrile illness 10 days prior. His magnetic resonance imaging (MRI) of the brain, cerebrospinal fluid (CSF) analysis, and other routine investigations were normal. The patient was treated with injectable methylprednisolone (1 g) given for five days along with other supportive therapy. A significant reduction in the opsoclonus, myoclonus, and ataxia was seen on a six-month follow-up. OMAS should be identified early to avoid the use of inappropriate medications, and immunotherapy must be provided as early as possible in order to prevent irreversible neurological damage., Competing Interests: The authors have declared that no competing interests exist., (Copyright © 2023, Shah et al.)

Background: A significant number of late middle-aged adults with depression have a high illness burden resulting from chronic conditions which put them at high risk of hospitalization. Many late middle-aged adults are covered by commercial health insurance, but such insurance claims have not been used to identify the risk of hospitalization in individuals with depression. In the present study, we developed and validated a non-proprietary model to identify late middle-aged adults with depression at risk for hospitalization, using machine learning methods., Methods: This retrospective cohort study involved 71,682 commercially insured older adults aged 55-64 years diagnosed with depression. National health insurance claims were used to capture demographics, health care utilization, and health status during the base year. Health status was captured using 70 chronic health conditions, and 46 mental health conditions. The outcomes were 1- and 2-year preventable hospitalization. For each of our two outcomes, we evaluated seven modelling approaches: four prediction models utilized logistic regression with different combinations of predictors to evaluate the relative contribution of each group of variables, and three prediction models utilized machine learning approaches - logistic regression with LASSO penalty, random forests (RF), and gradient boosting machine (GBM)., Results: Our predictive model for 1-year hospitalization achieved an AUC of 0.803, with a sensitivity of 72% and a specificity of 76% under the optimum threshold of 0.463, and our predictive model for 2-year hospitalization achieved an AUC of 0.793, with a sensitivity of 76% and a specificity of 71% under the optimum threshold of 0.452. For predicting both 1-year and 2-year risk of preventable hospitalization, our best performing models utilized the machine learning approach of logistic regression with LASSO penalty which outperformed more black-box machine learning models like RF and GBM., Conclusions: Our study demonstrates the feasibility of identifying depressed middle-aged adults at higher risk of future hospitalization due to burden of chronic illnesses using basic demographic information and diagnosis codes recorded in health insurance claims. Identifying this population may assist health care planners in developing effective screening strategies and management approaches and in efficient allocation of public healthcare resources as this population transitions to publicly funded healthcare programs, e.g., Medicare in the US., (© 2023. The Author(s).)

Cyanobacteria are photosynthetic Gram-negative, oxygen evolving prokaryotes with cosmopolitan distribution. Ultraviolet radiation (UVR) and other abiotic stresses result in DNA lesions in cyanobacteria. Nucleotide excision repair (NER) pathway removes the DNA lesions produced by UVR to normal DNA sequence. In cyanobacteria, detailed knowledge about NER proteins is poorly studied. Therefore, we have studied the NER proteins in cyanobacteria. Analyses of 289 amino acids sequence from 77 cyanobacterial species have revealed the presence of a minimum of one copy of NER protein in their genome. Phylogenetic analysis of NER protein shows that UvrD has maximal rate of amino acid substitutions which resulted in increased branch length. The motif analysis shows that UvrABC proteins is more conserved than UvrD, Further, UvrA with UvrB protein interacts with each other and form stable complex which have DNA binding domain on the surface of the complex. UvrB also have DNA binding domain. Positive electrostatic potential was found in the DNA binding region, which is followed by negative and neutral electrostatic potential. Additionally, the surface accessibility values at the DNA strands of T5-T6 dimer binding site were maximal. Protein nucleotide interaction shows the strong binding of T5-T6 dimer with NER proteins of Synechocystis sp. PCC 6803. This process repairs the UV-induced DNA lesions in dark when photoreactivation is inactive. Regulation of NER proteins protect cyanobacterial genome and maintain the fitness of organism under different abiotic stresses., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2023 Elsevier B.V. All rights reserved.)

The objective of this study was to investigate the potential association between the use of four frequently prescribed drug classes, namely antihypertensive drugs, statins, selective serotonin reuptake inhibitors, and proton-pump inhibitors, and the likelihood of disease progression from mild cognitive impairment (MCI) to dementia using electronic health records (EHRs). We conducted a retrospective cohort study using observational EHRs from a cohort of approximately 2 million patients seen at a large, multi-specialty urban academic medical center in New York City, USA between 2008 and 2020 to automatically emulate the randomized controlled trials. For each drug class, two exposure groups were identified based on the prescription orders documented in the EHRs following their MCI diagnosis. During follow-up, we measured drug efficacy based on the incidence of dementia and estimated the average treatment effect (ATE) of various drugs. To ensure the robustness of our findings, we confirmed the ATE estimates via bootstrapping and presented associated 95% confidence intervals (CIs). Our analysis identified 14,269 MCI patients, among whom 2501 (17.5%) progressed to dementia. Using average treatment estimation and bootstrapping confirmation, we observed that drugs including rosuvastatin (ATE = - 0.0140 [- 0.0191, - 0.0088], p value < 0.001), citalopram (ATE = - 0.1128 [- 0.125, - 0.1005], p value < 0.001), escitalopram (ATE = - 0.0560 [- 0.0615, - 0.0506], p value < 0.001), and omeprazole (ATE = - 0.0201 [- 0.0299, - 0.0103], p value < 0.001) have a statistically significant association in slowing the progression from MCI to dementia. The findings from this study support the commonly prescribed drugs in altering the progression from MCI to dementia and warrant further investigation., (© 2023. The Author(s).)

Objective: Diverticular disease (DD) is one of the most prevalent conditions encountered by gastroenterologists, affecting ~50% of Americans before the age of 60. Our aim was to identify genetic risk variants and clinical phenotypes associated with DD, leveraging multiple electronic health record (EHR) data sources of 91,166 multi-ancestry participants with a Natural Language Processing (NLP) technique., Materials and Methods: We developed a NLP-enriched phenotyping algorithm that incorporated colonoscopy or abdominal imaging reports to identify patients with diverticulosis and diverticulitis from multicenter EHRs. We performed genome-wide association studies (GWAS) of DD in European, African and multi-ancestry participants, followed by phenome-wide association studies (PheWAS) of the risk variants to identify their potential comorbid/pleiotropic effects in clinical phenotypes., Results: Our developed algorithm showed a significant improvement in patient classification performance for DD analysis (algorithm PPVs ≥ 0.94), with up to a 3.5 fold increase in terms of the number of identified patients than the traditional method. Ancestry-stratified analyses of diverticulosis and diverticulitis of the identified subjects replicated the well-established associations between ARHGAP15 loci with DD, showing overall intensified GWAS signals in diverticulitis patients compared to diverticulosis patients. Our PheWAS analyses identified significant associations between the DD GWAS variants and circulatory system, genitourinary, and neoplastic EHR phenotypes., Discussion: As the first multi-ancestry GWAS-PheWAS study, we showcased that heterogenous EHR data can be mapped through an integrative analytical pipeline and reveal significant genotype-phenotype associations with clinical interpretation., Conclusion: A systematic framework to process unstructured EHR data with NLP could advance a deep and scalable phenotyping for better patient identification and facilitate etiological investigation of a disease with multilayered data., Competing Interests: The authors have no conflicts to declare., (Copyright: © 2023 Joo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Several social determinants of health (SDoH) have been associated with the onset of major depressive disorder (MDD). However, prior studies largely focused on individual SDoH and thus less is known about the relative importance (RI) of SDoH variables, especially in older adults. Given that risk factors for MDD may differ across the lifespan, we aimed to identify the SDoH that was most strongly related to newly diagnosed MDD in a cohort of older adults., Methods: We used self-reported health-related survey data from 41 174 older adults (50-89 years, median age = 67 years) who participated in the Mayo Clinic Biobank, and linked ICD codes for MDD in the participants' electronic health records. Participants with a history of clinically documented or self-reported MDD prior to survey completion were excluded from analysis ( N = 10 938, 27%). We used Cox proportional hazards models with a gradient boosting machine approach to quantify the RI of 30 pre-selected SDoH variables on the risk of future MDD diagnosis., Results: Following biobank enrollment, 2073 older participants were diagnosed with MDD during the follow-up period (median duration = 6.7 years). The most influential SDoH was perceived level of social activity (RI = 0.17). Lower level of social activity was associated with a higher risk of MDD [hazard ratio = 2.27 (95% CI 2.00-2.50) for highest v. lowest level]., Conclusion: Across a range of SDoH variables, perceived level of social activity is most strongly related to MDD in older adults. Monitoring changes in the level of social activity may help identify older adults at an increased risk of MDD.

Objective: To evaluate if a machine learning approach can accurately predict antidepressant treatment outcome using electronic health records (EHRs) from patients with depression., Method: This study examined 808 patients with depression at a New York City-based outpatient mental health clinic between June 13, 2016 and June 22, 2020. Antidepressant treatment outcome was defined based on trend in depression symptom severity over time and was categorized as either "Recovering" or "Worsening" (i.e., non-Recovering), measured by the slope of individual-level Patient Health Questionnaire-9 (PHQ-9) score trajectory spanning 6 months following treatment initiation. A patient was designated as "Recovering" if the slope is less than 0 and as "Worsening" if the slope was no less than 0. Multiple machine learning (ML) models including L2 norm regularized Logistic Regression, Naive Bayes, Random Forest, and Gradient Boosting Decision Tree (GBDT) were used to predict treatment outcome based on additional data from EHRs, including demographics and diagnoses. Shapley Additive Explanations were applied to identify the most important predictors., Results: The GBDT achieved the best results of predicting "Recovering" (AUC: 0.7654 ± 0.0227; precision: 0.6002 ± 0.0215; recall: 0.5131 ± 0.0336). When excluding patients with low PHQ-9 scores (<10) at baseline, the results of predicting "Recovering" (AUC: 0.7254 ± 0.0218; precision: 0.5392 ± 0.0437; recall: 0.4431 ± 0.0513) were obtained. Prior diagnosis of anxiety, psychotherapy, recurrent depression, and baseline depression symptom severity were strong predictors., Conclusions: The results demonstrate the potential utility of using ML in longitudinal EHRs to predict antidepressant treatment outcome. Our predictive tool holds the promise to accelerate personalized medical management in patients with psychiatric illnesses., (© 2023 The Authors. Psychiatric Research and Clinical Practice published by Wiley Periodicals LLC on behalf of American Psychiatric Association.)

We used US nationwide commercial insurance claims data (2011-2015) to study the effect of social deprivation on clinical and demographic risk factors for suicidal ideation (SI) and suicide attempts (SA) among US youth and adults < 65 years, after having a mental health or substance use disorder-related outpatient encounter. Neighborhood social deprivation level was summarized by the quintile of social deprivation index (SDI) at individuals' zip code level. Cox proportional hazard models were used to evaluate the effect of social deprivation on demographic and clinical risk factors for SI and SA. The study cohort consisted of 317,383 individuals < 65 years, with 124,424 aged < 25 (youth) and 192,959 aged between 25 and 64 (adults). Neighborhood social deprivation impacted risk factors for SI and SA differently for youth and adults. Among youth, SDI interacted with multiple risk factors for both SI and SA. The effects of the risk factors were larger on youth from middle socioeconomic neighborhoods. Among adults, risk of SI was the strongest in the most deprived neighborhoods, but risk of SA did not vary by neighborhood deprivation level. Our findings suggest community-based suicide prevention initiatives should be tailored according to neighborhood deprivation level and the targeted individual's age to maximize the impact., (© 2023. The Author(s).)