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Depression is a multifactorial disease with unknown etiology affecting globally. It's the second most significant reason for infirmity in 2020, affecting about 50 million people worldwide, with 80% living in developing nations. Recently, a surge in depression research has been witnessed, resulting in a multitude of emerging techniques developed for prediction, evaluation, detection, classification, localization, and treatment. The main purpose of this study is to determine the volume of depression research conducted on different aspects such as genetics, proteins, hormones, oxidative stress, inflammation, mitochondrial dysfunction, and associations with other mental disorders like anxiety and stress using traditional and medical intelligence (medical with AI). In addition, it also designs a comprehensive survey on detection, treatment planning, and genetic predisposition, along with future recommendations. This work is designed through different methods, including a systematic mapping process, literature review, and network visualization. In addition, we also used VOSviewer software and some authentic databases such as Google Scholar, Scopus, PubMed, and Web of Science for data collection, analysis, and designing comprehensive picture of the study. We analyzed 60 articles related to medical intelligence, including 47 from machine learning with 513,767 subjects (mean ± SD = 10,931.212 ± 35,624.372) and 13 from deep learning with 37,917 subjects (mean ± SD = 3159.75 ± 6285.57). Additionally, we also found that stressors impact the brain's cognitive and autonomic functioning, resulting in increased production of catecholamine, decreased cholinergic and glucocorticoid activity, with increased cortisol. These factors lead to chronic inflammation and hinder the brain's normal functioning, leading to depression, anxiety, and cardiovascular disorders. In the brain, reactive oxygen species (ROS) production is increased by IL-6 stimulation and mitochondrial cytochrome c oxidase is inhibited by nitric oxide, a potent inhibitor. Proteins, lipids, oxidative phosphorylation enzymes, and mtDNA are further disposed to oxidative impairment in the mitochondria. Consequently, mitochondrial dysfunction exacerbates oxidative stress, impairs mitochondrial DNA (mtDNA) or deletions of mtDNA, increases intracellular Ca2+ levels, changes in fission/fusion and mitochondrial morphology, and lastly leads to neuronal death. This study highlights the multidisciplinary approaches to depression with different aspects using traditional and medical intelligence. It will open a new way for depression research through new emerging technologies. [ABSTRACT FROM AUTHOR]

Integrating machine learning into medical diagnostics has revolutionized the field, particularly enhancing Computer-aided Diagnosis (CAD) systems. These systems assist healthcare professionals by leveraging medical data and machine learning algorithms for more accurate diagnosis and treatment plans. Mammography, an X-ray-based imaging technique, is pivotal in early breast cancer detection, enabling the differentiation between benign and malignant lesions. Recent studies have focused on developing deep learning-enabled mammography CAD systems, which have shown promising results in detecting, segmenting, and classifying anomalies in mammogram images. This comprehensive review presents an innovative system architecture for breast cancer detection, segmentation, and classification using deep learning within mammography CAD systems. It also explores publicly available mammogram datasets and the critical parameters for assessing deep learning system performance. The literature review is meticulously conducted using the PRISMA methodology to evaluate and synthesise novel research findings in this domain. This survey highlights the technological advancements and underlines the potential of deep learning in transforming mammographic analysis for breast cancer detection.

Abstract We successfully employed targeted outreach to communities in Palm Beach County, Florida, to enhance detection of invasive reptiles. We defined targeted outreach as delivering a specific message to a specific audience, at a specific location, to obtain a specific result. These efforts improved our ability to solicit community involvement focused on target species of interest in locations at risk of potential establishment of incipient populations. From 2018 through 2020, we reached over 112,000 individuals who reported over 50 nonnative lizard sightings to EDDMapS, a web-based mapping system for documenting invasive species, and the State-managed IVE-GOT1 reporting hotline. We considered reports to be directly attributed to our outreach efforts when the reporter indicated our outreach method as the source from which they obtained information on reporting large invasive lizards. We found print media elicited the most reports, while social media reached the largest audience both in direct shares and spillover to additional communities outside our target area. We concluded that to help improve invasive species management programs, three tactics could be employed: (1) using multiple forms of media, (2) additional educational support to improve the accuracy of public reports, and (3) rapid, dedicated capability to respond to reported sightings.

Abstract The heterogeneity of Parkinson’s disease (PD) presents considerable challenges for accurate diagnosis, particularly during early-stage disease, when the symptoms may be extremely subtle. This study aimed to assess the accuracy of a convolutional neural network (CNN) technique based on the 6-min walk test (6MWT) measured using wearable sensors to distinguish patients with early-stage PD (n = 78) from healthy controls (n = 50). The participants wore six sensors, and performed the 6MWT. The time-series data were converted into new images. The results revealed that the gyroscopic vertical component of the lumbar spine displayed the highest classification accuracy of 83.5%, followed by those of the thoracic spine (83.1%) and right thigh (79.5%) segment. These findings suggest that the 6MWT and CNN models may facilitate earlier diagnosis and monitoring of PD symptoms, enabling clinicians to provide timely treatment during the critical transition from normal to pathologic gait patterns.

Abstract Circulating tumour cells (CTCs), derived from primary tumours, play a pivotal role in cancer metastasis by migrating into the peripheral bloodstream. These cells are paramount in clinical research, serving as early diagnostic markers for metastatic cancer. Analysing CTC counts and their biomarker characteristics can provide invaluable insights into tumour identification, profiling, and metastatic capabilities. However, the rarity and diverse nature of CTCs in the bloodstream present significant challenges to their isolation and detection, especially in the initial stages of metastasis. Recent advancements in nanotechnology have led to the development of innovative CTC separation and detection methods. This review focuses on applying nanoparticles, nanomaterials, and microfluidic platforms to simplify the isolation and detection of CTCs. The infusion of nanotechnology in this field marks a crucial turning point, enabling the necessary progress to advance CTC research.

Abstract Chronic kidney disease (CKD) is a global public health problem with major human and economic consequences. Despite advances in clinical guidelines, classification systems and evidence-based treatments, CKD remains underdiagnosed and undertreated and is predicted to be the fifth leading cause of death globally by 2040. This review aims to identify barriers and enablers to the effective detection, diagnosis, disclosure and management of CKD since the introduction of the Kidney Disease Outcomes Quality Initiative (KDOQI) classification in 2002, advocating for a renewed approach in response to updated Kidney Disease: Improving Global Outcomes (KDIGO) 2024 clinical guidelines. The last two decades of improvements in CKD care in the UK are underpinned by international adoption of the KDIGO classification system, mixed adoption of evidence-based treatments and research informed clinical guidelines and policy. Interpretation of evidence within clinical and academic communities has stimulated significant debate of how best to implement such evidence which has frequently fuelled and frustratingly forestalled progress in CKD care. Key enablers of effective CKD care include clinical classification systems (KDIGO), evidence-based treatments, electronic health record tools, financially incentivised care, medical education and policy changes. Barriers to effective CKD care are extensive; key barriers include clinician concerns regarding overdiagnosis, a lack of financially incentivised care in primary care, complex clinical guidelines, managing CKD in the context of multimorbidity, bureaucratic burden in primary care, underutilisation of sodium-glucose co-transporter-2 inhibitor (SGLT2i) medications, insufficient medical education in CKD, and most recently – a sustained disruption to routine CKD care during and after the COVID-19 pandemic. Future CKD care in UK primary care must be informed by lessons of the last two decades. Making step change, over incremental improvements in CKD care at scale requires a renewed approach that addresses key barriers to detection, diagnosis, disclosure and management across traditional boundaries of healthcare, social care, and public health. Improved coding accuracy in primary care, increased use of SGLT2i medications, and risk-based care offer promising, cost-effective avenues to improve patient and population-level kidney health. Financial incentives generally improve achievement of care quality indicators – a review of financial and non-financial incentives in CKD care is urgently needed.

ABSTRACTBackground Pseudomonas aeruginosa (P. aeruginosa) is widely distributed in air, soil, and water, human respiratory tract, intestinal tract, and skin. It can induce bloodstream infection, urinary tract infection, gastrointestinal tract infection, respiratory tract infection, etc. Conventional bacterial isolation, culture, and identification are time-consuming, and many false negative results, which cannot meet the needs of precise clinical diagnosis，and proper treatment. This study aims to develop a rapid isothermal amplification assay of Pseudomonas aeruginosa.Methods Specific primers were designed according to the National Center for Biotechnology Information (NCBI) database based on the highly conserved sequence of Pseudomonas aeruginosa virulence factor gene lecA, and a recombinase polymerase amplification (RPA) detection method was established. The sensitivity and specificity were calculated, as well as the collection and processing of clinical samples.Results The thermostatic amplification technique for Pseudomonas aeruginosa established in this paper allows nucleic acid detection within 10 minutes without cross-amplification with other bacterial strains. 27 P. aeruginosa infections were accurately detected in 300 clinical samples.Conclusion The rapid detection system based on thermostatic amplification had shown high sensitivity and specificity in this study, indicated that this method can effectively assist clinical bacterial detection.

This study reviews the recent progress of artificial intelligence for colonoscopy from detection to diagnosis. The source of data was 27 original studies in PubMed. The search terms were “colonoscopy” (title) and “deep learning” (abstract). The eligibility criteria were: (1) the dependent variable of gastrointestinal disease; (2) the interventions of deep learning for classification, detection and/or segmentation for colonoscopy; (3) the outcomes of accuracy, sensitivity, specificity, area under the curve (AUC), precision, F1, intersection of union (IOU), Dice and/or inference frames per second (FPS); (3) the publication year of 2021 or later; (4) the publication language of English. Based on the results of this study, different deep learning methods would be appropriate for different tasks for colonoscopy, e.g., Efficientnet with neural architecture search (AUC 99.8%) in the case of classification, You Only Look Once with the instance tracking head (F1 96.3%) in the case of detection, and Unet with dense-dilation-residual blocks (Dice 97.3%) in the case of segmentation. Their performance measures reported varied within 74.0–95.0% for accuracy, 60.0–93.0% for sensitivity, 60.0–100.0% for specificity, 71.0–99.8% for the AUC, 70.1–93.3% for precision, 81.0–96.3% for F1, 57.2–89.5% for the IOU, 75.1–97.3% for Dice and 66–182 for FPS. In conclusion, artificial intelligence provides an effective, non-invasive decision support system for colonoscopy from detection to diagnosis.

Breast cancer is an illness that affects many women and can cause even death; this is a case of not being detected on time, which could be due to a human error during the analysis of radiographic images or not going on time in a health center. For this, using machine learning (ML) to analyze radiographic images is proposed as a support tool for radiologists aiming to reduce false diagnostic rates. While researching information, it was detected that this technology has many benefits in the health area; however, it also has limitations or disadvantages. The importance of this paper is to demonstrate that there are not enough clinical tests nor details about the methodologies that were used; there should be more to assert that ML is defined at the moment of making a diagnosis, which generates no conclusive results regarding effectiveness and therefore creates mistrust in doctors, and some people might rather use deep learning (DL) for its application in the detection of breast cancer because DL has more practical tests and fewer limitations than machine learning. [ABSTRACT FROM AUTHOR]

Introduction Diarrhea is a significant health problem in the Third World. Identification of the pathogen that causes diarrhea is vital for measures to prevent and control this disease. There are also very few reports of diarrhea in Sudan. Our study aimed to determine the Prevalence of specific protozoan pathogens (Entamoeba histolytica, Cryptosporidium parvum., and Giardia spp) in children in Khartoum, Sudan. Methods We conducted a cross-sectional survey among children under five years of age hospitalized with acute diarrhea between April and December 2014. Diarrheal stool samples were collected, and E. histolytica, C. parvum, and Giardia spp were examined using multiplex real-time PCR. Results Four hundred and thirty-seven children with acute diarrhea were included in this study; the higher prevalence of diarrhea was in the age ≤ 2 years old (403, 92.2%), >2–≤4 years (32, 7.3%), and >4–

Abstract Background Retinal detachment (RD) is a vision-threatening disorder of significant severity. Individuals with high myopia (HM) face a 2 to 6 times higher risk of developing RD compared to non-myopes. The timely identification of high myopia-related retinal detachment (HMRD) is crucial for effective treatment and prevention of additional vision impairment. Consequently, our objective was to streamline and validate a machine-learning model based on clinical laboratory omics (clinlabomics) for the early detection of RD in HM patients. Methods We extracted clinlabomics data from the electronic health records for 24,440 HM and 5607 HMRD between 2015 and 2022. Lasso regression analysis assessed fifty-nine variables, excluding collinear variables (variance inflation factor > 10). Four models based on random forest, gradient boosting machine (GBM), generalized linear model, and Deep Learning Model were trained for HMRD diagnosis and employed for internal validation. An external test of the models was done. Three random data sets were further processed to validate the performance of the diagnostic model. The primary outcomes were the area under the receiver operating characteristic curve (AUC) and the area under the precision-recall curve (AUCPR) to diagnose HMRD. Results Nine variables were selected by all models. Given the AUC and AUCPR values across the different sets, the GBM model was chosen as the final diagnostic model. The GBM model had an AUC of 0.8550 (95%CI = 0.8322–0.8967) and an AUCPR of 0.5584 (95%CI = 0.5250–0.5879) in the training set. The AUC and AUCPR in the internal validation were 0.8405 (95%CI = 0.8060–0.8966) and 0.5355 (95%CI = 0.4988–0.5732). During the external test evaluation, it reached an AUC of 0.7579 (95%CI = 0.7340–0.7840) and an AUCPR of 0.5587 (95%CI = 0.5345–0.5880). A similar discriminative capacity was observed in the three random data sets. The GBM model was well-calibrated across all the sets. The GBM-RD model was implemented into a web application that provides risk prediction for HM individuals. Conclusion GBM algorithms based on nine features successfully predicted the diagnosis of RD in patients with HM, which will help ophthalmologists to establish a preliminary diagnosis and to improve diagnostic accuracy in the clinic.

Objective: The Case Fatality Rate (CFR) of Leptospirosis in Semarang City has shown a tendency to increase from 2019 to 2022. In 2022, there were 30 cases reported, with 8 deaths (CFR 27%). The purpose of this study was to detect Leptospira sp. bacteria in rats and analyze the risk factors for leptospirosis in Semarang City. Methods: This study employed an observational approach with a Case-Control Study design. The research was conducted in Semarang City from November to December 2023. Non-random sampling techniques were used, specifically the total sampling method, resulting in a sample size of 45 case groups and 45 control groups. Bivariate analysis was performed using the Chi-square test, with a significance level set at

Many people across the world have been infected with Epstein Barr virus (EBV), a kind of a human herpes virus. Loss of kidney function happens progressively with time in those with chronic kidney disease (CKD). Individuals with CKD may be more susceptible to viral infections such as EBV due to their compromised immune systems. The purpose of this research is to quantify the frequency with which EBV infection occurs in patients with CKD in the Al-Najaf governorate. Blood samples from 50 people with CKD and 50 healthy controls were collected for the study at the Specialized Centre for Diseases and Kidney Transplant at Al-Sadder Medical City in Al-Najaf city. Nineteen of the CKD patients were men, whereas 31 were women. The age range of individuals who took part in the research varied from 17 to 47. The time frame for this study’s data gathering was from January 2022 to December 2022. Fifty hemodialysis patients gave blood samples for analysis. Researchers measured anti-EBV IgG antibodies using the ELISA method to evaluate the efficacy of humoral immunity. Eleven (22% of all samples) were found to have positive results for Epstein Barr nuclear antigen2 (EBNA2) IgG. This number was 43.1% of all males and 38.4% of all females. However, the serum concentration was found to be statistically significantly higher in the age range of 17-26 years (p0.05) than in any other age group. The RT-qPCR method was also applied to detect EBV infection in the study. Only 18 (36% of the sample) of the 50 patients had positive RT-qPCR results for EBV. This number was 15.7% of all males and 48.4% of all females; however, the positivity rate increased to 71.4% in the (17-26) age group and decreased to 28.5% in those older than 57. Results from the control group were negative for EBNA2 IgG antibodies and EBV DNA. Findings of this research demonstrate that the prevalence of EBV antibodies is considerably higher among patients undergoing hemodialysis than in the group serving as a control, regardless of age. In addition, the results of the study support the use of real-time PCR for detecting EBV among patients with CKD, due to its high sensitivity and specificity.

Introduction Diarrhea is a significant health problem in the Third World. Identification of the pathogen that causes diarrhea is vital for measures to prevent and control this disease. There are also very few reports of diarrhea in Sudan. Our study aimed to determine the Prevalence of specific protozoan pathogens (Entamoeba histolytica, Cryptosporidium parvum., and Giardia spp) in children in Khartoum, Sudan. Methods We conducted a cross-sectional survey among children under five years of age who were hospitalized with acute diarrhea between April and December 2014. Diarrheal stool samples were collected and E. histolytica, C. parvum, and Giardia spp were examined using multiplex real-time PCR. Results Four hundred and thirty-seven children with acute diarrheawere included in this study; the higher Prevalence of diarrhea was in the age less than ≤ 2 years old (403,92.2%). The male-to-female ratio in this study was 1:1.7. infection with intestinal parasite was found in 155 (35.5%) cases, and co-infection was detected in 16 (10.3%) cases. Giardia spp(18.8%) and C. parvum (15.8 %) were the most frequently identified parasites, followed by E. histolytica (0.9). The parasite infection rate was highest and lowest in the under 2-year-old group (92.3%) and the 2–4-year-old group (7.3%). The infection rate was higher in boys (67.1%) than in girls (32.9%). The incidence of protozoan infection was higher in the rainy season (August to December) (92.2%), corresponding with that in the dry Season (April to June). (7.8%). Discussion Our present study demonstrated the high prevalence of Giardia spp and C. parvum in children with diarrhea in the Khartoum region and the usefulness of the multiplex real-time method in disclosing pathogenic protozoal agents. Our result highlighted the necessity of developing intervention measurement and control strategies to deal with childhood parasitic diarrhea in this region.

IntroductionSymptoms of psychosis, characterized by delusions and hallucinations, are commonly experienced by persons living with dementia. A systematic review was completed to identify tools to evaluate symptoms of psychosis compared to a reference standard in persons with dementia. Articles reporting correlation values between psychosis tools were also identified.MethodsThe search concepts psychosis, dementia, and diagnostic accuracy were used to search MEDLINE, PsycINFO, and Embase. Included articles meeting the primary objective described a tool to assess symptoms of psychosis, delusions, or hallucinations in persons with dementia, a reference standard form of diagnostic assessment for psychosis, and diagnostic accuracy outcomes for the psychosis tool. Secondary objective articles reported correlation values between two or more psychosis tools in persons with dementia.ResultsOne study met the primary objective and described the sensitivity and specificity of the Neuropsychiatric Inventory (NPI) and Columbia University Scale for Psychopathology in Alzheimer's Disease (CUSPAD) in identifying symptoms of psychosis, hallucinations, and delusions. The sensitivity of the NPI and CUSPAD in identifying symptoms of psychosis was 83 and 90%, respectively. Nine studies meeting the secondary objective described eleven unique tools and examined the degree to which tools used to assess psychotic symptoms in persons with dementia were related.DiscussionThe NPI and CUSPAD were identified in a single study as psychosis tools that have been evaluated against a reference standard of psychosis assessment in persons with dementia. Various tools to assess the burden of psychotic symptoms in persons with dementia exist, but the diagnostic accuracy of existing tools remains understudied. Further research on the comparative utility and diagnostic accuracy is required for all psychosis tools used with persons with dementia.

Obstructive sleep apnea (OSA) is negatively affecting the health of people and their quality of life. This breathing disorder has a multifactorial etiology. However, orofacial factors in OSA patients have been reported to be relatively more significant factors for its etiopathogenesis and its progression. Polysomnography has been a conventional diagnostic modality for OSA. However, in recent years, various novel and modern detection and diagnostic options have been reported in the current literature. Adenotonsillectomy, positive airway pressure therapy, oral appliances (e.g. mandibular advancement device), and pharmacological drugs (intranasal corticosteroids, etc.) have been known as some of the common treatment modalities for OSA. However, recently several modern therapeutic ways for OSA have been reported in the current literature. The aim of this review article is to comprehensively appraise and highlight the current and recent literature about several aspects of OSA, particularly its modern and novel detection methods and therapeutic options.

Abstract Over the past several decades, substantial ground has been gained in understanding the biology of sex differences. With new mandates to include sex as a biological variable in NIH-funded research, greater knowledge is forthcoming on how sex chromosomes, sex hormones, and social and societal differences between sexes can affect the pathophysiology of health and disease. A detailed picture of how biological sex impacts disease pathophysiology will directly inform clinicians in their treatment approaches and challenge canonical therapeutic strategies. Thus, a profound opportunity to explore sex as a variable in personalized medicine now presents itself. While many sex differences are apparent in humans and have been described at length, we are only beginning to see how such differences impact disease progression, treatment efficacy, and outcomes in obesity, type 2 diabetes, and cardiovascular disease. Here, we briefly present the most salient and convincing evidence of sex differences in type 2 diabetes detection, diagnostics, disease course, and therapeutics. We then offer commentary on how this evidence can inform clinicians on how to approach the clinical workup and management of different patients with diabetes. Finally, we discuss some gaps that remain in the literature and propose several research questions to guide basic and translational researchers as they continue in this growing area of scientific exploration.

Traumatic brain injury (TBI) represents a significant burden on individuals, societies, and healthcare systems as it is associated with long-term neurological and behavioral consequences. Although these effects vary according to severity, events that lead to TBI and mild TBI (mTBI), such as concussions, subconcussive impacts, and non-impact violent head movements, may also lead to similar changes. Soccer players are particularly prone to mTBI as they are exposed to head impacts in various ways during training and gameplay. A commonly-claimed TBI risk is that posed by the ''heading'' technique. Our examination of the literature questions the extent to which heading actually is a risk for TBI and mTBI vs other sources of head impact in soccer. Although headgear may protect against some impacts, it has not been widely adopted due to limited efficacy, practical limitations and potential changes to the heading technique. Nevertheless, accurate assessment of head impacts and other movements that may lead to TBI in soccer would be valuable to players, coaches, athletic and medical personnel. A potential method for accurately detecting head acceleration – a crucial element of most head injuries – is measurements through accelerometers. Here, we survey the different types of accelerometers and recent findings on their accuracy and feasibility among soccer players and offer suggestions for long-term research with these tools.

Mycotoxins, secondary metabolites synthesized by various filamentous fungi genera such as Aspergillus, Penicillium, Fusarium, Claviceps, and Alternaria, are potent toxic compounds. Their production is contingent upon specific environmental conditions during fungal growth. Arising as byproducts of fungal metabolic processes, mycotoxins exhibit significant toxicity, posing risks of acute or chronic health complications. Recognized as highly hazardous food contaminants, mycotoxins present a pervasive threat throughout the agricultural and food processing continuum, from plant cultivation to post-harvest stages. The imperative to adhere to principles of good agricultural and industrial practice is underscored to mitigate the risk of mycotoxin contamination in food production. In the domain of food safety, the rapid and efficient detection of mycotoxins holds paramount significance. This paper delineates conventional and commercial methodologies for mycotoxin detection in ensuring food safety, encompassing techniques like liquid chromatography, immunoassays, and test strips, with a significant emphasis on the role of electrochemiluminescence (ECL) biosensors, which are known for their high sensitivity and specificity. These are categorized into antibody-, and aptamer-based, as well as molecular imprinting methods. This paper examines the latest advancements in biosensors for mycotoxin testing, with a particular focus on their amplification strategies and operating mechanisms.

Abstract Foramen magnum stenosis is a serious, and potentially life-threatening complication of achondroplasia. The foramen magnum is smaller in infants with achondroplasia, compared with the general population, and both restricted growth in the first 2 years and premature closure of skull plate synchondroses can contribute to narrowing. Narrowing of the foramen magnum can lead to compression of the brainstem and spinal cord, and result in sleep apnoea and sudden death. There is a lack of clarity in the literature on the timing of regular monitoring for foramen magnum stenosis, which assessments should be carried out and when regular screening should be ceased. The European Achondroplasia Forum (EAF) is a group of clinicians and patient advocates, representative of the achondroplasia community. Members of the EAF Steering Committee were invited to submit suggestions for guiding principles for the detection and management of foramen magnum stenosis, which were collated and discussed at an open workshop. Each principle was scrutinised for content and wording, and anonymous voting held to pass the principle and vote on the level of agreement. A total of six guiding principles were developed which incorporate routine clinical monitoring of infants and young children, timing of routine MRI screening, referral of suspected foramen magnum stenosis to a neurosurgeon, the combination of assessments to inform the decision to decompress the foramen magnum, joint decision making to proceed with decompression, and management of older children in whom previously undetected foramen magnum stenosis is identified. All principles achieved the ≥ 75% majority needed to pass (range 89–100%), with high levels of agreement (range 7.6–8.9). By developing guiding principles for the detection and management of foramen magnum stenosis, the EAF aim to enable infants and young children to receive optimal monitoring for this potentially life-threatening complication.

Abstract Background The global burden of invasive fungal infections (IFIs) has shown an upsurge in recent years due to the higher load of immunocompromised patients suffering from various diseases. The role of early and accurate diagnosis in the aggressive containment of the fungal infection at the initial stages becomes crucial thus, preventing the development of a life-threatening situation. With the changing demands of clinical mycology, the field of fungal diagnostics has evolved and come a long way from traditional methods of microscopy and culturing to more advanced non-culture-based tools. With the advent of more powerful approaches such as novel PCR assays, T2 Candida, microfluidic chip technology, next generation sequencing, new generation biosensors, nanotechnology-based tools, artificial intelligence-based models, the face of fungal diagnostics is constantly changing for the better. All these advances have been reviewed here giving the latest update to our readers in the most orderly flow. Main text A detailed literature survey was conducted by the team followed by data collection, pertinent data extraction, in-depth analysis, and composing the various sub-sections and the final review. The review is unique in its kind as it discusses the advances in molecular methods; advances in serology-based methods; advances in biosensor technology; and advances in machine learning-based models, all under one roof. To the best of our knowledge, there has been no review covering all of these fields (especially biosensor technology and machine learning using artificial intelligence) with relevance to invasive fungal infections. Conclusion The review will undoubtedly assist in updating the scientific community’s understanding of the most recent advancements that are on the horizon and that may be implemented as adjuncts to the traditional diagnostic algorithms.

Abstract Background A growing body of research has emphasized 5-hydroxymethylcytosine (5hmC) as an important epigenetic mark. High-resolution methods to detect 5hmC require high sequencing depth and are therefore expensive. Many studies have used enrichment-based methods to detect 5hmC; however, conventional enrichment-based methods have limited resolution. To overcome these limitations, we developed EBS-seq, a cost-efficient method for 5hmC detection with single-base resolution that combines the advantages of high-resolution methods and enrichment-based methods. Results EBS-seq uses selective labeling of 5hmC, deamination of cytosine and 5-methylcytosine, pull-down of labeled 5hmC, and C-to-T conversion during DNA amplification. Using this method, we profiled 5hmC in HEK293T cells and two colorectal cancer samples. Compared with conventional enrichment-based 5hmC detection, EBS-seq improved 5hmC signals by localizing them at single-base resolution. Furthermore, EBS-seq was able to determine 5hmC levels in CpG-dense regions where distortion of signals can occur, such as CpG islands and CpG shores. Comparing EBS-seq and conventional high-resolution 5hmC detection by ACE-seq, we showed that EBS-seq is more effective at finding 5hmC sites. Using EBS-seq, we found strong associations between gene expression and gene-body 5hmC content in both HEK293T cells and colorectal cancer samples. Conclusions EBS-seq is a reliable and cost-efficient method for 5hmC detection because it simultaneously enriches 5hmC-containing DNA fragments and localizes 5hmC signals at single-base resolution. This method is a promising choice for 5hmC detection in challenging clinical samples with low 5hmC levels, such as cancer tissues.

Abstract Background Several studies have been conducted on the effect of interventions on the detection of depression in primary healthcare (PHC). Systematic reviews have also been done on the effectiveness of separate interventions. However, systematic reviews are not done on the comparative effectiveness of several interventions. This study, therefore, aimed at synthesizing the global evidence on the effectiveness of interventions to improve the detection of depression in PHC. Methods We searched PubMed, Embase, PsycINFO, Web of Science, Cochrane Database of Systematic Reviews, Global Index Medicus, African Index Medicus, and African Journals Online, from the inception of the databases to until the 4th week of April 2020. We also searched references of the included articles. We included randomized trials, cluster randomized trials, or quasi-experimental studies, which evaluated the effectiveness of an intervention to improve detection of depression in the PHC setting. Two of the review authors independently extracted data from the included studies. The methodological quality of the included studies was assessed using the Assessment Tool for Quantitative Studies developed by the Effective Public Health Practice Project. The protocol for the review was registered on PROSPERO (CRD42020166291). Results Of 23,305 records identified, we included 58 articles in the review. Diverse types of interventions were evaluated to improve clinician diagnosis of depression in the PHC setting. Interventions related to implementation of guidelines, screening with feedback, educational interventions which incorporated active learning and clinical practice, and disclosure of screening results were found to be mostly effective. Interventions which combined education, screening, and feedback were particularly more effective. Most of the included studies were weak or moderate in their methodological quality. Conclusions Our review indicates that implementation of a single type of intervention does not improve the detection of depression in PHC. Combining aspects of each type of intervention which are more effective may be useful. Education and training interventions which include more simulation and role playing are found to be effective over time. Most of the studies conducted in the area are from high-income countries and are weak in their methodological quality. There is need to conduct more number of studies in low-income settings.

Shiga-toxin-producing Escherichia coli (STEC) causes a wide spectrum of diseases including hemorrhagic colitis and hemolytic uremic syndrome (HUS). The current Food Safety Inspection Service (FSIS) testing methods for STEC use the Food and Drug Administration (FDA) Bacteriological Analytical Manual (BAM) protocol, which includes enrichment, cell plating, and genomic sequencing and takes time to complete, thus delaying diagnosis and treatment. We wanted to develop a rapid, sensitive, and potentially portable assay that can identify STEC by detecting Shiga toxin (Stx) using the CANARY (Cellular Analysis and Notification of Antigen Risks and Yields) B-cell based biosensor technology. Five potential biosensor cell lines were evaluated for their ability to detect Stx2. The results using the best biosensor cell line (T5) indicated that this biosensor was stable after reconstitution with assay buffer covered in foil at 4 °C for up to 10 days with an estimated limit of detection (LOD) of ≈0.1–0.2 ng/mL for days up to day 5 and ≈0.4 ng/mL on day 10. The assay detected a broad range of Stx2 subtypes, including Stx2a, Stx2b, Stx2c, Stx2d, and Stx2g but did not cross-react with closely related Stx1, abrin, or ricin. Additionally, this assay was able to detect Stx2 in culture supernatants of STEC grown in media with mitomycin C at 8 and 24 h post-inoculation. These results indicate that the STEC CANARY biosensor developed in this study is sensitive, reproducible, specific, rapid (≈3 min), and may be applicable for surveillance of the environment and food to protect public health.

Novel SARS-CoV-2 variants have multiple mutations that may impact molecular diagnostics. The markedly conserved S2 subunit may be utilized to detect new variants. A comparison of 694 specimens (2019–2022) in Thailand using a commercial RT-PCR kit and the kit in combination with S2 primers and a probe was performed. Delayed amplification in ORF1ab was detected in one BA.4 omicron, whereas no amplification problem was encountered in the S2 target. There were no statistically significant differences in mean Ct value between the target genes (E, N, ORF1ab, and S2) and no significant differences in mean Ct value between the reagents. Furthermore, 230,821 nucleotide sequences submitted by 20 representative counties in each region (Jan–Oct 2022) have been checked for mutations in S2 primers and probe using PrimerChecker; there is a very low chance of encountering performance problems. The S2 primers and probe are still bound to the top five currently circulating variants in all countries and Thailand without mismatch recognition (Jun–Nov 2023). This study shows the possible benefits of detecting S2 in combination with simultaneously detecting three genes in a kit without affecting the Ct value of each target. The S2 subunit may be a promising target for the detection of SARS-CoV-2 variants with multiple mutations.

Citrus crops are affected by many fungal diseases. Among them, Citrus Black Spot caused by the ascomycete Phyllosticta citricarpa is particularly economically damaging wherever it occurs. Many other species of Phyllosticta are described on Citrus, but only P. citricarpa is considered a quarantine pest on the European continent. In order to prevent the introduction of this species into Europe, it is essential to have a detection test which can reliably identify it, and not confuse it with other species present on citrus, notably P. paracitricarpa. The latter taxon has recently been described as very close to P. citricarpa, and most detection tests do not allow to distinguish the two species. In this work, we exploited the genomic data of 37 isolates of Phyllosticta spp. from citrus, firstly to assess their phylogenetic relationships, and secondly to search for genomic regions that allowed the definition of species-specific markers of P. citricarpa. Analysis of 51 concatenated genes separated P. citricarpa and P. paracitricarpa in two phylogenetic clades. A locus was selected to define a hydrolysis probe and primers combination that could be used in real-time PCR for the specific detection of the quarantine species, to the exclusion of all others present on Citrus. This test was then thoroughly validated on a set of strains covering a wide geographical diversity, and on numerous biological samples to demonstrate its reliability for regulatory control. The validation data highlighted the need to check the reliability of the test in advance, when a change of reagents was being considered.

Viruses existed in wastewaters might pose a biosecurity risk to human and animal health. However, it is generally difficult to detect viruses in wastewater directly as they usually occur in low numbers in water. Therefore, processing large volumes of water to concentrate viruses in a much smaller final volume for detection is necessary. Glass wool has been recognized as an effective material to concentrate multiple in water, and in this study, we assessed the use of glass wools on concentrating pseudorabies virus (PRV), African swine fever virus (ASFV), and porcine epidemic diarrhea virus (PEDV) in water samples. The influence of pH values, water matrix, water volume, filtration rate, temperature on the effect of the method concentrating these viruses for detection was evaluated in laboratory. Our results revealed that glass wool was suitable for the concentration of above-mentioned viruses from different water samples, and demonstrated a good application effect for water with pH between 6.0–9.0. Furthermore, glass wool also showed a good recovery effect on concentrating viral nucleic acids and viral particles, as well as living viruses. In addition, combining use of glass wool with skim milk, polyethylene glycol (PEG)-NaCl, or ultracentrifuge had good effects on concentrating ASFV, PRV, and PEDV. Detection of wastewater samples (n = 70) collected from 70 pig farms in 13 regions across Hubei Province in Central China after glass-wool-concentration determined one sample positive for ASFV, eighteen samples positive for PRV, but no sample positive for PEDV. However, these positive samples were detected to be negative before glass wool enrichment was implemented. Our results suggest that glass wool-based water concentration method developed in this study represents an effective tool for detecting viruses in wastewater.

Heteroplasmy is the presence of two or more organellar genomes (mitochondrial or plastid DNA) in an organism, tissue, cell or organelle. Heteroplasmy can be detected by visual inspection of Sanger sequencing chromatograms, where it appears as multiple peaks of fluorescence at a single nucleotide position. Visual inspection of chromatograms is both consuming and highly subjective, as heteroplasmy is difficult to differentiate from background noise. Few software solutions are available to automate the detection of point heteroplasmies, and those that are available are typically proprietary, lack customization or are unsuitable for automated heteroplasmy assessment in large datasets. Here, we present PHFinder, a Python-based, open-source tool to assist in the detection of point heteroplasmies in large numbers of Sanger chromatograms. PHFinder automatically identifies point heteroplasmies directly from the chromatogram trace data. The program was tested with Sanger sequencing data from 100 humpback whales (Megaptera novaeangliae) tissue samples with known heteroplasmies. PHFinder detected most (90%) of the known heteroplasmies thereby greatly reducing the amount of visual inspection required. PHFinder is flexible and enables explicit specification of key parameters to infer double peaks (i.e., heteroplasmies).

Decisions about noisy stimuli are widely understood to be made by accumulating evidence up to a decision bound that can be adjusted according to task demands. However, relatively little is known about how such mechanisms operate in continuous monitoring contexts requiring intermittent target detection. Here, we examined neural decision processes underlying detection of 1 s coherence targets within continuous random dot motion, and how they are adjusted across contexts with weak, strong, or randomly mixed weak/strong targets. Our prediction was that decision bounds would be set lower when weak targets are more prevalent. Behavioural hit and false alarm rate patterns were consistent with this, and were well captured by a bound-adjustable leaky accumulator model. However, beta-band EEG signatures of motor preparation contradicted this, instead indicating lower bounds in the strong-target context. We thus tested two alternative models in which decision-bound dynamics were constrained directly by beta measurements, respectively, featuring leaky accumulation with adjustable leak, and non-leaky accumulation of evidence referenced to an adjustable sensory-level criterion. We found that the latter model best explained both behaviour and neural dynamics, highlighting novel means of decision policy regulation and the value of neurally informed modelling.

Early diagnosis is the most important determinant of oral and oropharyngeal squamous cell carcinoma (OPSCC) outcomes, yet most of these cancers are detected late, when outcomes are poor. Typically, nonspecialists such as dentists screen for oral cancer risk, and then they refer high-risk patients to specialists for biopsy-based diagnosis. Because the clinical appearance of oral mucosal lesions is not an adequate indicator of their diagnosis, status, or risk level, this initial triage process is inaccurate, with poor sensitivity and specificity. The objective of this study is to provide an overview of emerging optical imaging modalities and novel artificial intelligence-based approaches, as well as to evaluate their individual and combined utility and implications for improving oral cancer detection and outcomes. The principles of image-based approaches to detecting oral cancer are placed within the context of clinical needs and parameters. A brief overview of artificial intelligence approaches and algorithms is presented, and studies that use these 2 approaches singly and together are cited and evaluated. In recent years, a range of novel imaging modalities has been investigated for their applicability to improving oral cancer outcomes, yet none of them have found widespread adoption or significantly affected clinical practice or outcomes. Artificial intelligence approaches are beginning to have considerable impact in improving diagnostic accuracy in some fields of medicine, but to date, only limited studies apply to oral cancer. These studies demonstrate that artificial intelligence approaches combined with imaging can have considerable impact on oral cancer outcomes, with applications ranging from low-cost screening with smartphone-based probes to algorithm-guided detection of oral lesion heterogeneity and margins using optical coherence tomography. Combined imaging and artificial intelligence approaches can improve oral cancer outcomes through improved detection and diagnosis.

Context: The undiagnosed and untreated tuberculosis (TB) cases underpin the experience of accelerating deaths. Everyone should be engaged in managing TB patients to revert the current trend. Aims: In this context, we assessed the outcome of an education intervention on the knowledge of TB and referral practice of presumptive TB cases to directly observed therapy strategy amongst patent medicine vendors (PMVs). Settings and Design: The study was quasi-experimental, conducted amongst 647 PMVs in Delta State, Nigeria. Methods: A structured questionnaire was administered to obtain baseline data on knowledge of TB. An educational intervention on knowledge of TB was then given. A post-intervention assessment of TB knowledge was subsequently conducted using the same questionnaire. The exact number of referrals of presumptive TB cases by PMVs and the number that tested positive amongst the referred presumptive TB cases, 3 months before and after the training were obtained from the state TB database. Statistical Analysis Used: SPSS v. 26 was used for data analysis. Results: The pre-training knowledge average score was 15.45 ± 6.45, while the post-training average score was 19.44 ± 7.03 (P < 0.001). The pre-training average number of presumptive cases referred was 146 ± 124.7, and the post-training was 205.67 ± 255.4, P = 0.41. The pre-training average number of cases that turned out positive was 9.5 ± 6.3, and the post-training was 13.5 ± 11.3, P = 0.42. Conclusion: There was a significant improvement in PMVs' knowledge of TB post-intervention. However, while an increase in the number of cases referred and positives detected was observed, this was not significant. Periodic training and updates to PMVs in keeping with current trends and best practices in TB management are recommended.

Background: Chlamydia trachomatis (CT) infection affects over 131 million people annually. It is one of the most prevalent bacterial sexually transmitted infections. This study aimed at the detection of CT infection among young adult females in the Okpoko community, Anambra State, Nigeria. Patients and Methods: Self-collected vaginal swabs from 288 females aged 18–37 years were tested for CT antigen using the Solid Chlamydia Rapid Test kit (USA). Results: CT antigen was detected in 19 (6.6%) samples. The age group of 23–27 years had the highest prevalence rate (10.7%), followed by 18–22 years (8.7%). The married participants were more infected (8.3%) than singles (3.1%) (P < 0.05). A higher percentage of participants (99.3%) were not aware that CT was sexually transmitted. Conclusion: Active CT infection was detected in the Okpoko community. Regular screening of young females as well as propagation of information on sexually transmitted infections should be encouraged to break the community transmission.

Is early cancer detection using deep learning models reliable? The creation of expert systems based on Deep Learning can become an asset for the achievement of an early detection, offering a preliminary diagnosis or a second opinion, as if it were a second specialist, thus helping to reduce the mortality rate of cancer patients. In this work, we study the differences and impact of various optimizers and hyperparameters in a Convolutional Neural Network model, to then be tested on different datasets. The results of the tests are analyzed and an implementation of a cancer classification model is proposed focusing on the different approaches of the selected Optimizers as the best method for the achievement of optimal results in accurately improving the detection of cancerous cells. Cancer, despite being considered one of the biggest health problems worldwide, continues to be a major problem because its cause remains unknown. Regular medical check-ups are not frequent in countries where access to specialized health services is not affordable or easily accessible, leading to detection in more advanced stages when the symptoms are quite visible. To reduce cases and mortality rates ensuring early detection is paramount.

Background: The International Caries Detection and Assessment System (ICDAS) was developed to integrate several criteria systems into one standard system for caries detection and assessment. The aim of this study was to identify Turkish dental practitioners’ perceptions and experience about ICDAS II and assess how they could affect clinical decision-making.Methods: A web-based data collection form, including demographic characteristics, experience of caries detection systems, and two different clinical images with caries and treatment options, was given to Turkish dental practitioners. Data were analyzed with the chi-square test and logistic regression using SPSS 22.0 software (IBM, Chicago, IL) at a significance level of P < 0.05.Results: Data collection forms were completed by 382 general dental practitioners. For the first clinical scenario 70.7% of the practitioners decided that no treatment was required. For the second clinical scenario 89.5% of the practitioners decided to perform tooth restoration. Considering the clinical scenario 2 treatment options, while practitioners working in the public hospital marked amalgam restoration at a higher rate, practitioners working in private clinics marked composite resin restoration and root canal treatment at a higher rate (P < 0.05). With regard to effects on treatment choices for clinical scenarios, binary logistic regression analysis found no significant effects of gender, age, or institution (P > 0.05).Conclusion: The visual caries detection system, ICDAS II, was a useful tool in standardizing caries diagnostic skills for practitioners and improving decision-making abilities on caries treatment.

Abstract Background The diagnostic results of magnetic resonance imaging (MRI) are essential references for arthroscopy as an invasive procedure. A deviation between medical imaging diagnosis and arthroscopy results may cause irreversible damage to patients and lead to excessive medical treatment. To improve the accurate diagnosis of meniscus injury, it is urgent to develop auxiliary diagnosis algorithms to improve the accuracy of radiological diagnosis. Purpose This study aims to present a fully automatic 3D deep convolutional neural network (DCNN) for meniscus segmentation and detects arthroscopically proven meniscus tears. Materials and methods Our institution retrospectively included 533 patients with 546 knees who underwent knee magnetic resonance imaging (MRI) and knee arthroscopy. Sagittal proton density-weighted (PDW) images in MRI of 382 knees were regarded as a training set to train our 3D-Mask RCNN. The remaining data from 164 knees were used to validate the trained network as a test set. The masks were hand-drawn by an experienced radiologist, and the reference standard is arthroscopic surgical reports. The performance statistics included Dice accuracy, sensitivity, specificity, FROC, receiver operating characteristic (ROC) curve analysis, and bootstrap test statistics. The segmentation performance was compared with a 3D-Unet, and the detection performance was compared with radiological evaluation by two experienced musculoskeletal radiologists without knowledge of the arthroscopic surgical diagnosis. Results Our model produced strong Dice coefficients for sagittal PDW of 0.924, 0.95 sensitivity with 0.823 FPs/knee. 3D-Unet produced a Dice coefficient for sagittal PDW of 0.891, 0.95 sensitivity with 1.355 FPs/knee. The difference in the areas under 3D-Mask-RCNN FROC and 3D-Unet FROC was statistically significant (p = 0.0011) by bootstrap test. Our model detection performance achieved an area under the curve (AUC) value, accuracy, and sensitivity of 0.907, 0.924, 0.941, and 0.785, respectively. Based on the radiological evaluations, the AUC value, accuracy, sensitivity, and specificity were 0.834, 0.835, 0.889, and 0.754, respectively. The difference in the areas between 3D-Mask-RCNN ROC and radiological evaluation ROC was statistically significant (p = 0.0009) by bootstrap test. 3D Mask RCNN significantly outperformed the 3D-Unet and radiological evaluation demonstrated by these results. Conclusions 3D-Mask RCNN has demonstrated efficacy and precision for meniscus segmentation and tear detection in knee MRI, which can assist radiologists in improving the accuracy and efficiency of diagnosis. It can also provide effective diagnostic indicators for orthopedic surgeons before arthroscopic surgery and further promote precise treatment.

Bone tumours are a rare and often highly malignant entity. Early clinical diagnosis is the most important step, but the difficulty of detecting and assessing bone malignancies is in its radiological peculiarity and limited experience of non-experts. Since X-ray imaging is the first imaging method of bone tumour diagnostics, the purpose of this study is to develop an artificial intelligence (AI) model to detect and segment the tumorous tissue in a radiograph. We investigated which methods are necessary to cope with limited and heterogeneous data. We collected 531 anonymised radiographs from our musculoskeletal tumour centre. In order to adapt to the complexity of recognizing the malignant tissue and cope with limited data, transfer learning, data augmentation as well as several architectures, some of which were initially designed for medical images, were implemented. Furthermore, dataset size was varied by adding another bone tumour entity. We applied a data split of 72%, 18%, 10% for training, validation and testing, respectively. To provide statistical significance and robustness, we applied a cross-validation and image stratification with respect to tumour pixels present. We achieved an accuracy of 99.72% and an intersection over union of 87.43% for hold-out test data by applying several methods to tackle limited data. Transfer learning and additional data brought the greatest performance increase. In conclusion, our model was able to detect and segment tumorous tissue in radiographs with good performance, although it was trained on a very limited amount of data. Transfer Learning and data augmentation proved to significantly mitigate the issue of limited data samples. However, to accomplish clinical significance, more data has to be acquired in the future. Through minor adjustments, the model could be adapted to other musculoskeletal tumour entities and become a general support tool for orthopaedic surgeons and radiologists.

Early diagnosis and detection of disease progression are critical to successful therapeutic intervention in glaucoma, the leading cause of irreversible blindness worldwide. Optical coherence tomography (OCT) is a non-invasive imaging technique that allows objective quantification in vivo of key glaucomatous structural changes in the retina and the optic nerve head (ONH). Advances in OCT technology have increased the scan speed and enhanced image quality, contributing to early glaucoma diagnosis and monitoring, as well as the visualization of critically important structures deep within the ONH, such as the lamina cribrosa. OCT angiography (OCTA) is a dye-free technique for noninvasively assessing ocular microvasculature, including capillaries within each plexus serving the macula, peripapillary retina and ONH regions, as well as the deeper vessels of the choroid. This layer-specific assessment of the microvasculature has provided evidence that retinal and choroidal vascular impairments can occur during early stages of glaucoma, suggesting that OCTA-derived measurements could be used as biomarkers for enhancing detection of glaucoma and its progression, as well as to reveal novel insights about pathophysiology. Moreover, these innovations have demonstrated that damage to the macula, a critical region for the vision-related quality of life, can be observed in the early stages of glaucomatous eyes, leading to a paradigm shift in glaucoma monitoring. Other advances in software and hardware, such as artificial intelligence-based algorithms, adaptive optics, and visible-light OCT, may further benefit clinical management of glaucoma in the future. This article reviews the utility of OCT and OCTA for glaucoma diagnosis and disease progression detection, emphasizes the importance of detecting macula damage in glaucoma, and highlights the future perspective of OCT and OCTA. We conclude that the OCT and OCTA are essential glaucoma detection and monitoring tools, leading to clinical and economic benefits for patients and society.

Objective: To explore the quality and safety of testing in the city′s regional medical laboratories for COVID-19 nucleic acid detection. Methods: To strengthen the quality control of the city′s medical laboratories for COVID-19 nucleic acid detection through monthly inspections, squatting inspections, emergency inspections, blind sample surveys, sample retention, and retesting. Results: Regional medical laboratories for COVID-19 nucleic acid detection were non-compliant in terms of personnel qualifications, business training, internal quality control, result judgement, retesting processes, report uploading, cleaning and disinfection, and medical waste disposal. The number of laboratories conducting COVID-19 nucleic acid testing gradually expanded from 29 at the beginning of the outbreak on March 18 to 98 on May 16, but the number of non-compliant laboratories did not increase significantly. The pass rate of the blind sample survey of the city′s medical testing laboratories ranged from 96.6% to 100.0%, and the concurrence rate between the results of the laboratory′s retained samples for retesting and the results of the retained testing facilities ranged from 98.3% to 100.0%. Conclusions: The quality of regional COVID-19 nucleic acid detection in the city is generally controllable, with no systematic quality problems. The quality control model with multiple measures is worthy of application in the quality control of regional COVID-19 nucleic acid detection.

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) community transmission was eliminated in Australia from 1/11/2020 to 30/6/2021, allowing evaluation of surveillance system performance in detecting novel outbreaks, including against variants of concern (VoCs). This paper aims to define system requirements for coronavirus disease 2019 (COVID-19) surveillance under future transmission and response scenarios, based on surveillance system performance to date. Methods This study described and evaluated surveillance systems and epidemiological characteristics of novel outbreaks based on publicly available data, and assessed surveillance system sensitivity and timeliness in outbreak detection. These findings were integrated with analysis of other critical COVID-19 public health measures to establish future COVID-19 management requirements. Results Twenty-five epidemiologically distinct outbreaks and five distinct clusters were identified in the study period, all linked through genomic sequencing to novel introductions from international travellers. Seventy percent (21/30) were detected through community testing of people with acute respiratory illness, and 30% (9/30) through quarantine screening. On average, 2.07% of the State population was tested in the week preceding detection for those identified through community surveillance. From 17/30 with publicly available data, the average time from seeding to detection was 4.9 days. One outbreak was preceded by unexpected positive wastewater results. Twenty of the 24 outbreaks in 2021 had publicly available sequencing data, all of which identified VoCs. A surveillance strategy for future VoCs similar to that used for detecting SARS-CoV-2 would require a 100–1000-fold increase in genomic sequencing capacity compared to the study period. Other essential requirements are maintaining outbreak response capacity and developing capacity to rapidly engineer, manufacture, and distribute variant vaccines at scale. Conclusions Australia’s surveillance systems performed well in detecting novel introduction of SARS-CoV-2 while community transmission was eliminated; introductions were infrequent and case numbers were low. Detection relied on quarantine screening and community surveillance in symptomatic members of the general population, supported by comprehensive genomic sequencing. Once vaccine coverage is maximised, future COVID-19 control should shift to detection of SARS-CoV-2 VoCs, requiring maintenance of surveillance systems and testing all international arrivals, alongside greatly increased genomic sequencing capacity. Effective government support of localised public health response mechanisms and engagement of all sectors of the community is crucial to current and future COVID-19 management.

Abstract Background Depression is one of the commonest mental disorders in primary care but is poorly identified. The objective of this review was to determine the level of detection of depression by primary care clinicians and its determinants in studies from low- to middle-income countries (LMICs). Methods A systematic review and meta-analysis was conducted using PubMed, PsycINFO, MEDLINE, EMBASE, LILAC, and AJOL with no restriction of year of publication. Risk of bias within studies was evaluated with the Effective Public Health Practice Project (EPHPP). “Gold standard” diagnosis for the purposes of this review was based on the 9-item Patient Health Questionnaire (PHQ-9; cutoff scores of 5 and 10), other standard questionnaires and interview scales or expert diagnosis. Meta-analysis was conducted excluding studies on special populations. Analyses of pooled data were stratified by diagnostic approaches. Results A total of 3159 non-duplicate publications were screened. Nine publications, 2 multi-country studies, and 7 single-country studies, making 12 country-level reports, were included. Overall methodological quality of the studies was good. Depression detection was 0.0% in four of the twelve reports and < 12% in another five. PHQ-9 was the main tool used: the pooled detection in two reports that used PHQ-9 at a cutoff point of 5 (combined sample size = 1426) was 3.9% (95% CI = 2.3%, 5.5%); in four reports that used PHQ-9 cutoff score of 10 (combined sample size = 5481), the pooled detection was 7.0% (95% CI = 3.9%, 10.2%). Severity of depression and suicidality were significantly associated with detection. Conclusions While the use of screening tools is an important limitation, the extremely low detection of depression by primary care clinicians poses a serious threat to scaling up mental healthcare in LMICs. Interventions to improve detection should be prioritized. Systematic review registration PROSPERO CRD42016039704 .

Early detection of Mycoplasmal mastitis is greatly hampered by late seroconversion, slow growth of Mycoplasma organisms, intermittent shedding, and the high cost of diagnostic tests. To improve future diagnostic development, examining the available techniques is necessary. Accordingly, the present study systematically reviewed M. bovis diagnostic studies published between January 2000 and April 2023 utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol. The protocol registration was performed according to the Open Science Framework (osf.io/ug79h), and the electronic search was conducted in the World Catalog, Mendeley, ProQuest, ScienceDirect, Semantic Scholar, PubMed, Google Scholar, Prime Scholar, and PubMed Central databases using a Boolean operator and inclusion and exclusion criteria. Of the 1194 pieces of literature retrieved, 67 studies were included. Four broad categories of up to 16 diagnostic approaches were reported: microbial culture, serological, DNA-based, and mass spectrometry. Overall, DNA-based techniques were the most published (48.0%), with recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP) as the most promising user-friendly, equipment-free techniques. On the other hand, mass spectrometry was reported as the least utilized (2.9%) given the high equipment cost. Though costly and laboratory-allied, DNA-based techniques, particularly PCRs, were reported as the most rapid and specific approach.

The summer temperatures recorded in Poland in 2022 were among the highest in over 30 years and, combined with higher-than-expected rainfall, gave the impression of an almost tropical climate. Such climatic conditions were ideal for the transmission of vector-borne zoonotic diseases such as West Nile fever. In northeastern Poland, in the Mazowieckie region, the Polish event-based surveillance network reported increased fatalities of free-living hooded crows (Corvus corone cornix). West Nile virus (WNV) lineage 2 was identified for the first time as the etiological agent responsible for the death of the birds. WNV was detected in 17 out of the 99 (17.17%) free-living birds tested in this study. All the WNV-infected dead birds were collected in the same area and were diagnosed in September by the NVRI and confirmed by the EURL for equine diseases, ANSES, in October 2022. Unnaturally high temperatures recorded in Poland in 2022 likely favored the infection and spread of the virus in the avian population. A nationwide alert and awareness raising of blood transfusion centers and hospitals was carried out to prevent human infections by WNV.

Nanomaterials have the characteristics of small size and high specific surface area. These characteristics make it unique from other traditional materials in the properties of light, sound, electricity, heat, and magnetism. With the continuous development of nanomaterials and the needs of clinical medicine, more and more functionalized nanomaterials and related devices have been widely used in medical detection with the advantages of high sensitivity, high specificity, accuracy, and speed. The rapid development of nanomaterials can provide new methods for clinical diagnosis and provide more scientific basis for disease prevention and treatment. Nanomaterials have great development potential and broad application prospects in the diagnosis of a variety of diseases. In this article, the progress in the medical detection and diagnosis of infection, tumor, diabetes, neurodegenerative disease and cardiovascular diseases is reviewed; future perspectives are discussed as well.

Background: The impact of the acquisition of Toxoplasmosis from the environment through ingestion of oocyst from the soil, food, or water contamination remains worrisome worldwide. Objective: This study aimed to apply the molecular method for the detection of the occurrence of Toxoplasma oocysts by polymerase chain reaction (PCR) from different soil samples in Duhok province, Kurdistan region of Iraq. Materials and Methods: In this study, the occurrence of soil contamination with Toxoplasma gondii oocysts was recorded in different Duhok City’s areas by collecting 144 soil samples from different places. Results: Only seven samples (4.86%) were identified as T. gondii oocysts using microscopical identification and confirmed at the molecular level via PCR analysis using a fragment of 122 bp as a target of B1 gene. No significant difference was observed among soil types; parks showed the highest percentage 2.77%, followed by playgrounds (1.38%), whereas rubbish dumps accounted for 0.96%. Conclusion: The current study showed, a low percentage of T. gondii oocysts are recorded in soil samples as compared with other studies worldwide, but there is still the risk of infection among people who ramble at these places remains, especially among children who play with soil at these places.

Purpose: The purpose of current study was to investigate the value of the late-phase enhancement features of pancreas contrast-enhanced ultrasound (CEUS) in the detection of liver metastases in patients with pancreatic ductal adenocarcinomas (PDAC).Methods: From October 2020 to March 2021, 86 patients were prospectively enrolled. The gold standard of liver metastatic and PDAC lesions were based on histopathologically diagnoses and multiple imaging modalities results. B-mode ultrasound (BMUS) was performed to detect suspected liver metastases before CEUS. During the late phase of pancreas CEUS, the entire liver was scanned again to detect hypoenhanced liver metastases. Liver metastases were confirmed by biopsy and histopathological results. The number and size of liver metastases detected by BMUS and during CEUS late phase were recorded and compared.Results: Suspected liver metastases were detected in 14 patients by BMUS (n = 23). During the late phase of CEUS, hypoenhanced liver metastases were detected in 23 patients (n = 87). When compared with BMUS, whole-liver scan during the late phase of CEUS detected more isoechoic, small, or superficially located lesions. Compared with BMUS, the specificity, sensitivity, positive predictive value, and negative predictive value of CEUS in diagnosing of liver metastases in PDAC patients were significantly improved (96.72% vs. 100%, 48% vs. 92%, 85.71% vs. 100%, and 83.10% vs. 96.83%, respectively) (P < 0.05).Conclusion: The late phase whole liver scan during CEUS of pancreas helps to detect more liver metastases, which is important for further clinical decision-making.

Background: Microsoft Windows Security is a recently implemented safeguard for the Windows operating systems, including the latest versions of Windows10 and 11. However, there is a major shortcoming in this system to stop Advanced Persistent Threat (APT). These are government-financed groups that are funded to attack other government entities. Following the initial security breach, the hacked Windows device is used to access the rest of the network devices in order to transfer data to external storage (Exfiltration). Methods: In this work, we have tested the Microsoft Windows Security system using MITRE CALDERA and ATT&CK frameworks and explain how APT groups are able to bypass Windows Security. Results: In this study we used "54ndc47" agent through GoLang feature in MITRE CALDERA platform to test and bypass Microsoft Windows Security systems (MS Windows 10). Through it, we were able to bypass the Windows Security system and display entire files in the victim's device. Conclusions: In this paper, we have provided recommendations to Microsoft to improve their Windows Security tool through the use of Artificial intelligence (AI).

Objectives to define the possibilities of diagnosing tuberculosis in patients of pulmonology hospital, including patients with HIV infection. Material and methods. During this retrospective one-step study, we analyzed 103 medical records of patients in pulmonology departments who had received a full range of laboratory and instrumental examinations, including the Mantoux test and the test with a recombinant tuberculosis allergen (Diaskintest). The patients were divided into two groups depending on their HIV status: Group 1 included 78 HIV-negative patients, Group 2 consisted of 25 HIV-positive patients. Results. A microscopy of sputum and bronchial lavage did not reveal acid-resistant mycobacteria in all patients. PCR test for M. tb DNA was positive in three patients in Group 1 (33.3 13.9% of the total number of examined patients) and in one patient in Group 2 (25.0 13.9%) (2 = 0.01; p = 0.931). According to the results of immunodiagnostics, a positive normergic reaction to the Mantoux test was observed in 24 patients in Group 1 and in 7 patients in Group 2 (30.8 5.2% and 28.9 9.0% respectively), 2 = 0.04; p = 0.846. The reaction for recombinant tuberculosis allergen (Diaskintest) was positive in 9 patients in Group 1 (11.5 3.6%) and in 5 patients in Group 2 (20.0 8.0%), 2 = 0.85; p = 0.358. Based on clinical, laboratory, instrumental and immunological examinations by a TB doctor, in total 12 patients (11.7%) were diagnosed with tuberculosis. These patients were distributed among the study groups as follows: 9 people (11.5 3.6%) in Group 1 and 3 people (12.0 6.5%) in Group 2 (2 = 0.0; p = 0.956). Conclusion. The inclusion of the Mantoux test and the reaction for recombinant tuberculosis allergen (Diaskintest) in the set of diagnostic tests for patients of the pulmonology department of the general hospital simplified the differential diagnosis of tuberculosis and pneumonia, both for a pulmonologist and a TB specialist.

Background Banana bunchy top virus (BBTV), cucumber mosaic virus (CMV) and banana streak virus (BSV) are important banana viruses, there are possible infections frequently with several viruses in field. Since the viruses are readily trasmitted in vegetative propagules, which pose a threat to banana production in banana-growing areas. Methods A multiplex polymerase chain reaction (PCR) protocol combined with LiquiChip analysis to identify BSV, BBTV, and CMV, with consistent amplification of plant ubiquitin (UBQ), the banana plant messenger RNA used as a procedural control. Multiplex reverse transcription (RT)-PCR amplicons were extended by allele-specific primers, followed by hybridization with carboxylated microspheres containing unique fluorescent oligonucleotides, which were detected using the LiquiChip 200 workstation. Results In this study, we aimed to develop a rapid, sensitive, and simultaneous detection method for BSV, BBTV, and CMV using a bead-based multiplex assay that can be applied in routine diagnosis. We demonstrated that this detection system was extremely efficient and highly specialized for differentiating individual in a mixture of viruses while being ten times more sensitive than traditional RT-PCR. The development of this method makes it feasible to detect banana viruses in field collected leaf samples.

Introduction: The increasing prevalence of type 2 Diabetes Mellitus (DM) can be done from identifying those with prediabetes and offer early interventions by utilising prescreening diagnostic tools. Machine learning algorithms and big data mining approaches have been postulated for predictive disease modelling in hospital and clinical settings. Aim: To outline the relative performance accuracies in predicting prediabetes conditions in different machine learning algorithms. Materials and Methods: A systematic literature search was conducted at Universiti of Kebangsaan, Kuala Lumpur, Malaysia, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) review protocol, and the research question was formulated based on the keywords of “Prediabetes” (Population), “Internet of Things” and “prediction model” (Intervention) and “screening” and “risk” (Outcome). International Prospective Register of Systematic Reviews (PROSPERO) registration (CRD42021264947) was done and databases were screened on 10th-24th June 2021 via Web of Science, Scopus, PubMed, Ovid and EBSCOhost. Inclusion criteria was English language prediction studies published between 2011-2021. Review articles, editorials, proceedings, commentary articles and articles not focusing on prediabetes were excluded. The quality of the articles was ranked via the Prediction Model Risk of Bias Assessment Tool (PROBAST). Results: A total of five articles that were published in 2014-2021 were included. The sample sizes ranged from 570 to 24,331 participants. Three studies (South Korea, United State of America (USA), Japan) suggested the applicability of the screening score prediction models for use in clinical settings related to personalised risk assessment and targeted interventions, with the predictors used being suitable for either the clinic or hospital. The simplicity of gender, age, Body Mass Index (BMI), blood pressure and waist circumference as predictors suggested that they can be utilised by the community. Conclusion: This review highlights the fact that the heterogeneity of the population used and validation issues may affect generalisation. Future studies should address these concerns to guide advocacy among healthcare providers in clinical practice as well as in data and expertise sharing for developing and validating urgently needed prediabetic prediction models.