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Abstract Deep learning-based methods for generating functional proteins address the growing need for novel biocatalysts, allowing for precise tailoring of functionalities to meet specific requirements. This advancement leads to the development of highly efficient and specialized proteins with diverse applications across scientific, technological, and biomedical fields. This study establishes a pipeline for protein sequence generation with a conditional protein diffusion model, namely CPDiffusion, to create diverse sequences of proteins with enhanced functions. CPDiffusion accommodates protein-specific conditions, such as secondary structures and highly conserved amino acids. Without relying on extensive training data, CPDiffusion effectively captures highly conserved residues and sequence features for specific protein families. We applied CPDiffusion to generate artificial sequences of Argonaute (Ago) proteins based on the backbone structures of wild-type (WT) Kurthia massiliensis Ago (KmAgo) and Pyrococcus furiosus Ago (PfAgo), which are complex multi-domain programmable endonucleases. The generated sequences deviate by up to nearly 400 amino acids from their WT templates. Experimental tests demonstrated that the majority of the generated proteins for both KmAgo and PfAgo show unambiguous activity in DNA cleavage, with many of them exhibiting superior activity as compared to the WT. These findings underscore CPDiffusion’s remarkable success rate in generating novel sequences for proteins with complex structures and functions in a single step, leading to enhanced activity. This approach facilitates the design of enzymes with multi-domain molecular structures and intricate functions through in silico generation and screening, all accomplished without the need for supervision from labeled data.

Abstract Fixed-point equations in the functional renormalization group approach are integrated from large to vanishing field, where an asymptotic potential in the limit of large field is implemented as initial conditions. This approach allows us to obtain a global fixed-point potential with high numerical accuracy, that incorporates the correct asymptotic behavior in the limit of large field. Our calculated global potential is in good agreement with the Taylor expansion in the region of small field, and it also coincides with the Laurent expansion in the regime of large field. Laurent expansion of the potential in the limit of large field for general case, that the spatial dimension d is a continuous variable in the range $$2\le d \le 4$$ 2 ≤ d ≤ 4 , is obtained. Eigenfunctions and eigenvalues of perturbations near the Wilson–Fisher fixed point are computed with the method of eigenperturbations. Critical exponents for different values of d and N of the O(N) universality class are calculated.

Abstract Telemedicine has gained popularity due to the increasing use of technology in our lives. However, no studies have explored the demographic factors affecting acceptability, desirability and adherence (ADA) to telemedicine in Singapore. Our study aims to evaluate the level of ADA of telemedicine services within demographic factors and to explore the association of potential demographic factors with the degree of acceptability, desirability and adherence of telemedicine among older adults in Singapore. A cross-sectional study was conducted with Singapore citizens or permanent residents aged 40–99 years, who were able to provide informed consent. Interviewers conducted door-to-door surveys in 67 Blocks of Housing & Development Board flats in Singapore, offering a self-administered electronic questionnaire available in four languages. Random sampling without replacement determined the order of blocks, floors and units visited. The questionnaire utilised Qtelemediab scoring and covered sociodemographic data, usage of telemedicine, as well as ADA towards telemedicine. A total of 324 valid responses were analysed. Increased age was associated with a significant decrease across all three domains of ADA namely acceptability (β = − 0.02, 95%CI − 0.03; − 0.02, p-value = 0.002), desirability (β = − 0.02, 95%CI − 0.02; − 0.02, p-value

Abstract Background Highly transmissible viruses including SARS-CoV-2 frequently accumulate novel mutations that are detected via high-throughput sequencing. However, there is a need to develop an alternative rapid and non-expensive approach. Here we developed a novel multiplex DNA detection method Intelli-OVI for analysing existing and novel mutations of SARS-CoV-2. Methods We have developed Intelli-OVI that includes the micro-disc-based method IntelliPlex and computational algorithms of objective variant identification (OVI). More than 250 SARS-CoV-2 positive samples including wastewater ones were analysed to verify the efficiency of the method. Results IntelliPlex uses micro-discs printed with a unique pictorial pattern as a labelling conjugate for DNA probes, and OVI allows simultaneous identification of several variants using multidimensional data obtained by the IntelliPlex method. Importantly, de novo mutations can be identified by decreased signals, which indicates that there is an emergence of de novo variant virus as well as prompts the need to design additional primers and probes. We have upgraded probe panel according to the emergence of new variants and demonstrated that Intelli-OVI efficiently identified more than 20 different SARS-CoV-2 variants by using 35 different probes simultaneously. Conclusions Intelli-OVI can be upgraded to keep up with rapidly evolving viruses as we showed in this study using SARS-CoV-2 as an example and may be suitable for other viruses but would need to be validated.

Abstract Objective Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. Methods Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. Results Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model’s predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. Conclusion This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

Abstract Protein language models (PLMs) play a dominant role in protein representation learning. Most existing PLMs regard proteins as sequences of 20 natural amino acids. The problem with this representation method is that it simply divides the protein sequence into sequences of individual amino acids, ignoring the fact that certain residues often occur together. Therefore, it is inappropriate to view amino acids as isolated tokens. Instead, the PLMs should recognize the frequently occurring combinations of amino acids as a single token. In this study, we use the byte-pair-encoding algorithm and unigram to construct advanced residue vocabularies for protein sequence tokenization, and we have shown that PLMs pre-trained using these advanced vocabularies exhibit superior performance on downstream tasks when compared to those trained with simple vocabularies. Furthermore, we introduce PETA, a comprehensive benchmark for systematically evaluating PLMs. We find that vocabularies comprising 50 and 200 elements achieve optimal performance. Our code, model weights, and datasets are available at https://github.com/ginnm/ProteinPretraining . Scientific contribution This study introduces advanced protein sequence tokenization analysis, leveraging the byte-pair-encoding algorithm and unigram. By recognizing frequently occurring combinations of amino acids as single tokens, our proposed method enhances the performance of PLMs on downstream tasks. Additionally, we present PETA, a new comprehensive benchmark for the systematic evaluation of PLMs, demonstrating that vocabularies of 50 and 200 elements offer optimal performance. Graphical Abstract

Abstract Background Knee osteoarthritis (KOA) has become a public health issue. Several systematic reviews (SRs) and meta-analyses (MAs) indicate that traditional Chinese exercise (TCE) may be an effective treatment for reducing pain and stiffness and improving physical function in people with knee osteoarthritis (KOA). Objectives To evaluate the literature quality and evidence for the systematic reviews of TCE for KOA and provide evidence to support the clinical application of TCE for KOA. Methods Eight databases were searched from their inception to January 3, 2023, to retrieve relevant literature, including China National Knowledge Infrastructure (CNKI), Wanfang, Chinese Scientific Journal Database (VIP), China Biology Medical literature database (CBM), PubMed, Embase, Web of Science and Cochrane Library, without restrictions on publication date or language. AMSTAR-2 and PRISMA 2020 assessed the methodological and reporting quality of included SRs/MAs. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system was utilized to evaluate the quality of evidence. Results A total of 18 SRs/MAs were included. The methodological quality was “very low” based on AMSTAR-2. The overall reporting quality was deficient based on PRISMA 2020. The quality of Chinese and English literature differed, with English literature being superior in methodological and reporting quality. Among 93 pieces of evidence obtained, 46 (49.46%) were of very low quality, 34 (36.56%) were of low quality, 13 (13.98%) were of moderate quality, and none were of high quality. TCE was supported by 76 pieces of evidence (81.72%). Conclusion TCE appears beneficial and safe for managing KOA. However, due to the relatively low methodological and evidentiary quality of included SRs/MAs, clinicians should interpret these findings cautiously.

Abstract Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Objective: Speech recognition technology is widely used as a mature technical approach in many fields. In the study of depression recognition, speech signals are commonly used due to their convenience and ease of acquisition. Though speech recognition is popular in the research field of depression recognition, it has been little studied in somatisation disorder recognition. The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies. To this end, we introduced our somatisation disorder speech database and gave benchmark results. Methods: By collecting speech samples of somatisation disorder patients, in cooperation with the Shenzhen University General Hospital, we introduced our somatisation disorder speech database, the Shenzhen Somatisation Speech Corpus (SSSC). Moreover, a benchmark for SSSC using classic acoustic features and a machine learning model was proposed in our work. Results: To obtain a more scientific benchmark, we compared and analysed the performance of different acoustic features, i. e., the full ComPare feature set, or only Mel frequency cepstral coefficients (MFCCs), fundamental frequency (F0), and frequency and bandwidth of the formants (F1-F3). By comparison, the best result of our benchmark was the 76.0% unweighted average recall achieved by a support vector machine with formants F1–F3. Conclusion: The proposal of SSSC may bridge a research gap in somatisation disorder, providing researchers with a publicly accessible speech database. In addition, the results of the benchmark could show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders.

The protein dynamical transition at ~200 K, where the biomolecule transforms from a harmonic, non-functional form to an anharmonic, functional state, has been thought to be slaved to the thermal activation of dynamics in its surface hydration water. Here, by selectively probing the dynamics of protein and hydration water using elastic neutron scattering and isotopic labeling, we found that the onset of anharmonicity in the two components around 200 K is decoupled. The one in protein is an intrinsic transition, whose characteristic temperature is independent of the instrumental resolution time, but varies with the biomolecular structure and the amount of hydration, while the one of water is merely a resolution effect.

Abstract Background Childhoods in urban or rural environments may differentially affect the risk of neuropsychiatric disorders, possibly through memory processing and neural response to emotional stimuli. Genetic factors may not only influence individuals’ choices of residence but also modulate how the living environment affects responses to episodic memory. Methods We investigated the effects of childhood urbanicity on episodic memory in 410 adults (discovery sample) and 72 adults (replication sample) with comparable socioeconomic statuses in Beijing, China, distinguishing between those with rural backgrounds (resided in rural areas before age 12 and relocated to urban areas at or after age 12) and urban backgrounds (resided in cities before age 12). We examined the effect of childhood urbanicity on brain function across encoding and retrieval sessions using an fMRI episodic memory paradigm involving the processing of neutral or aversive pictures. Moreover, genetic association analyses were conducted to understand the potential genetic underpinnings that might contribute to memory processing and neural mechanisms influenced by early-life urban or rural environments. Results Episodic memory retrieval accuracy for more difficult neutral stimuli was similar between those with urban and rural childhoods, whereas aversive stimuli elicited higher retrieval accuracy in the urban group (P = 0.023). For aversive stimuli, subjects with urban childhood had relatively decreased engagement of the striatum at encoding and decreased engagement of the hippocampus at retrieval. This more efficient striatal encoding of aversive stimuli in those with urban childhoods was associated with common variation in neurotrophic tyrosine kinase receptor type 2 (NTRK2) (right striatum: P = 1.58×10−6). These findings were confirmed in the replication sample. Conclusions We suggest that this differential striatal processing of aversive stimuli observed in individuals with urban or rural childhoods may represent mechanisms by which childhood urbanicity may affect brain circuits, heightening behavioral responses to negative stressors associated with urban environments. NTRK2-associated neural processes in the striatum may play a role in these processes.

Abstract Objective This study aimed to develop and validate an artificial intelligence radiopathological model using preoperative CT scans and postoperative hematoxylin and eosin (HE) stained slides to predict the pathological staging of gastric cancer (stage I-II and stage III). Methods This study included a total of 202 gastric cancer patients with confirmed pathological staging (training cohort: n = 141; validation cohort: n = 61). Pathological histological features were extracted from HE slides, and pathological models were constructed using logistic regression (LR), support vector machine (SVM), and NaiveBayes. The optimal pathological model was selected through receiver operating characteristic (ROC) curve analysis. Machine learnin algorithms were employed to construct radiomic models and radiopathological models using the optimal pathological model. Model performance was evaluated using ROC curve analysis, and clinical utility was estimated using decision curve analysis (DCA). Results A total of 311 pathological histological features were extracted from the HE images, including 101 Term Frequency-Inverse Document Frequency (TF-IDF) features and 210 deep learning features. A pathological model was constructed using 19 selected pathological features through dimension reduction, with the SVM model demonstrating superior predictive performance (AUC, training cohort: 0.949; validation cohort: 0.777). Radiomic features were constructed using 6 selected features from 1834 radiomic features extracted from CT scans via SVM machine algorithm. Simultaneously, a radiopathomics model was built using 17 non-zero coefficient features obtained through dimension reduction from a total of 2145 features (combining both radiomics and pathomics features). The best discriminative ability was observed in the SVM_radiopathomics model (AUC, training cohort: 0.953; validation cohort: 0.851), and clinical decision curve analysis (DCA) demonstrated excellent clinical utility. Conclusion The radiopathomics model, combining pathological and radiomic features, exhibited superior performance in distinguishing between stage I-II and stage III gastric cancer. This study is based on the prediction of pathological staging using pathological tissue slides from surgical specimens after gastric cancer curative surgery and preoperative CT images, highlighting the feasibility of conducting research on pathological staging using pathological slides and CT images.

Abstract Determinants of HIV-1 latency establishment are yet to be elucidated. HIV reservoir comprises a rare fraction of infected cells that can survive host and virus-mediated killing. In vitro reporter models so far offered a feasible means to inspect this population, but with limited capabilities to dissect provirus silencing dynamics. Here, we describe a new HIV reporter model, HIV-Timer of cell kinetics and activity (HIV-Tocky) with dual fluorescence spontaneous shifting to reveal provirus silencing and reactivation dynamics. This unique feature allows, for the first time, identifying two latent populations: a directly latent, and a recently silenced subset, with the latter having integration features suggestive of stable latency. Our proposed model can help address the heterogeneous nature of HIV reservoirs and offers new possibilities for evaluating eradication strategies.

Abstract The issue of high energy consumption in exhibition buildings has long been a focal point in the field of architectural design. However, current energy consumption assessments for exhibition buildings mainly focus on post-construction evaluations, lacking corresponding guidance during the initial design phase. To address this issue, this study selected 48 well-known exhibition buildings both domestically and internationally as research subjects. Utilizing scatter plot curve fitting, it was discovered that there exists a nonlinear quadratic relationship between the building area of the first floor and the courtyard area. Based on this relationship, four typical layouts were established to match the climatic characteristics of Hangzhou, a representative region in the Yangtze River Delta of China. Taking into account regional architectural features, the study specifically examined the impact of different orientations and window-to-wall ratios on energy consumption levels. The influence of these factors on energy consumption was analyzed using the DesignBuilder software. The results revealed that there exists an optimal window-to-wall ratio for exhibition buildings, with parallel, L-shaped enclosed south-facing courtyards, and U-shaped enclosed east-facing courtyards showing greater energy efficiency. This research provides guidance for designing exhibition buildings that are energy-efficient and foster a harmonious indoor–outdoor relationship.

Abstract Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage.

Catastrophic occurrences induced by disasters often lead to fatalities, extensive damage, and societal disruptions. In pursuit of realizing disaster-resilient smart cities, video surveillance systems incorporating artificial intelligence (AI) can automatically process and classify the disaster content in real-time. This advancement is fueled by the recent progress in computer vision and AI algorithms, specifically deep learning neural networks, which can be leveraged for disaster categorization tasks. However, minimizing the complexity of AI models while preserving accurate disaster classification remains a formidable challenge. In this paper, we propose a convolutional neural network-long short-term memory (CNN-LSTM) model capable of discerning four types of natural disasters and a non-disaster event. Contrary to prior research that treats input video as a sequence of independent frames, we demonstrate the significance of spatio-temporal characteristics in reaping high prediction accuracy. Furthermore, conventional methods rely on resource-intensive hardware to boost AI model performance, which may not suit real-time monitoring. To facilitate real-time disaster monitoring applications, the trained model is further optimized by utilizing a neural network acceleration platform known as OpenVINO. Our findings reveal that the optimized version of the proposed CNN-LSTM model sustains 100% accuracy while boosting throughput by 25% in terms of frames per second (FPS).

Excessive mining and utilization fossil fuels has led to drastic environmental consequences, which will contribute to global warming and cause further climate change with severe consequences for the human population. The magnitude of these challenges requires several approaches to develop sustainable alternatives for chemicals and fuels production. In this context, biological processes, mainly microbial fermentation, have gained particular interest. For example, autotrophic gas-fermenting acetogenic bacteria are capable of converting CO, CO2 and H2 into biomass and multiple metabolites through Wood-Ljungdahl pathway, which can be exploited for large-scale fermentation processes to sustainably produce bulk biochemicals and biofuels (e.g. acetate and ethanol) from syngas. Clostridium autoethanogenum is one representative of these chemoautotrophic bacteria and considered as the model for the gas fermentation. Recently, the development of synthetic biology toolbox for this strain has enabled us to study and genetically improve their metabolic capability in gas fermentation. In this review, we will summarize the recent progress involved in the understanding of physiological mechanism and strain engineering for C. autoethanogenum, and provide our perspectives on the future development about the basic biology and engineering biology of this strain.

ObjectiveThis study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients.MethodsData from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models.ResultsThe LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase.ConclusionThis study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

Historically, the central nervous system (CNS) was regarded as ‘immune-privileged’, possessing its own distinct immune cell population. This immune privilege was thought to be established by a tight blood-brain barrier (BBB) and blood-cerebrospinal-fluid barrier (BCSFB), which prevented the crossing of peripheral immune cells and their secreted factors into the CNS parenchyma. However, recent studies have revealed the presence of peripheral immune cells in proximity to various brain-border niches such as the choroid plexus, cranial bone marrow (CBM), meninges, and perivascular spaces. Furthermore, emerging evidence suggests that peripheral immune cells may be able to infiltrate the brain through these sites and play significant roles in driving neuronal cell death and pathology progression in neurodegenerative disease. Thus, in this review, we explore how the brain-border immune niches may contribute to the pathogenesis of neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS). We then discuss several emerging options for harnessing the neuroimmune potential of these niches to improve the prognosis and treatment of these debilitative disorders using novel insights from recent studies.

Purpose: To explore the mechanism by which Remazolam affects the phenotype and function of astrocytes to improve traumatic brain injury (TBI). Methods: The oxygen -glucose deprivation/recovery (OGD/R) cell model was constructed to simulate the pathological state of astrocytes in a TBI environment. The viability of astrocytes was measured by CCK-8, and the cytoskeleton changes were observed by Phalloidin- TRITC staining. The expressions of differentiation markers, Cx43 and phosphorylated Cx43 (P-Cx43) of A1/A2 astrocytes were detected by Western blot, and the complement C3 and S100A10 of A1/A2 astrocytes were detected by ELISA. The TBI rat model was established. The water content of brain tissue was measured by dry-wet specific gravity method, the pathological morphology of brain tissue in cortical injury area was observed by HE staining method, ROS was detected by fluorescence quantitative method, Cx43 expression was detected by immunohistochemistry method, and the differentiation markers of A1/A2 astrocytes were detected by immunofluorescence. Results: In the TBI environment, astrocytes showed decreased cell viability, blurred skeleton, and increased expression of Cx43. In TBI rats, the water content of brain tissue increased, the brain tissue in the cortex injury area was seriously damaged, ROS and Cx43 expression were significantly increased, and mainly distributed in A2 astrocytes. Remazolam can reverse the above results after the intervention. Conclusion: Remazolam affects the phenotype and function of astrocytes to improve TBI via regulating Cx43, and plays a role in protecting the neurological function of TBI rats.

Abstract Background The existing literature indicates that repetitive transcranial magnetic stimulation (rTMS) can potentially enhance the prognosis of poststroke aphasia (PSA). Nevertheless, these investigations did not identify the most effective parameters or settings for achieving optimal treatment outcomes. This study involved a meta-analysis aimed to identify the optimal variables for rTMS in treating post-infarction aphasia to guide the use of rTMS in rehabilitating PSA. Methods PubMed, Embase, and Cochrane Library databases were searched from inception to May 2023, and articles were reviewed manually using subject words and free words and supplemented with references from the included literature to obtain additional relevant literature. The search terms included “poststroke aphasia” and “repetitive transcranial magnetic stimulation (rTMS)” repetitive transcranial magnetic stimulation. Additionally, a review of the reference lists of previously published systematic reviews identified through the Cochrane Database of Systematic Reviews (search terms: poststroke aphasia, rTMS; restrictions: none) and PubMed (search terms: poststroke aphasia, rTMSs; restrictions: systematic review or meta-analysis) was performed. Information from studies involving different doses of rTMS in PSA was independently screened and extracted by 2 researchers. Results This meta-analysis included 387 participants with PSA across 18 randomized controlled trials. The results showed that the total pulse had a trend toward a significant correlation with the treatment effect (P = 0.088), while all other variables did not correlate significantly. When rTMS was not grouped by stimulus parameter and location, our nonlinear results showed that when the total pulses were 40,000 (standardized mean difference (SMD):1.86, 95% credible interval (CrI) 0.50 to 3.33), the pulse/session was 1000 (SMD:1.05, 95% CrI 0.55–1.57), and an RMT of 80% (SMD:1.08, 95% CrI 0.60–1.57) had the best treatment effect. When rTMS was grouped by stimulus parameters and location, our nonlinear results showed that when the total low-frequency (LF)-rTMS-right inferior frontal gyrus (RIFG) pulse was 40,000 (SMD:1.76, 95% CrI:0.36–3.29), the pulse/session was 1000 (SMD:1.06, 95% CrI:0.54–1.59). Optimal results were obtained with an RMT of 80% (SMD:1.14, 95% CrI 0.54 − 1.76). Conclusions The optimal treatment effects of rTMS for PSA may be obtained with a total pulse of 40,000, a pulse/session of 1000, and an RMT of 80%. Further rigorous randomized controlled studies are required to substantiate the validity of these results.

Abstract Growing high-quality core-shell heterostructure nanowires is still challenging due to the lattice mismatch issue at the radial interface. Herein, a versatile strategy is exploited for the lattice-mismatch-free construction of III-V/chalcogenide core-shell heterostructure nanowires by simply utilizing the surfactant and amorphous natures of chalcogenide semiconductors. Specifically, a variety of III-V/chalcogenide core-shell heterostructure nanowires are successfully constructed with controlled shell thicknesses, compositions, and smooth surfaces. Due to the conformal properties of obtained heterostructure nanowires, the wavelength-dependent bi-directional photoresponse and visible light-assisted infrared photodetection are realized in the type-I GaSb/GeS core-shell heterostructure nanowires. Also, the enhanced infrared photodetection is found in the type-II InGaAs/GeS core-shell heterostructure nanowires compared with the pristine InGaAs nanowires, in which both responsivity and detectivity are improved by more than 2 orders of magnitude. Evidently, this work paves the way for the lattice-mismatch-free construction of core-shell heterostructure nanowires by chemical vapor deposition for next-generation high-performance nanowire optoelectronics.

Abstract Objective This retrospective observational study aims to develop and validate artificial intelligence (AI) pathomics models based on pathological Hematoxylin–Eosin (HE) slides and pathological immunohistochemistry (Ki67) slides for predicting the pathological staging of colorectal cancer. The goal is to enable AI‐assisted accurate pathological staging, supporting healthcare professionals in making efficient and precise staging assessments. Methods This study included a total of 267 colorectal cancer patients (training cohort: n = 213; testing cohort: n = 54). Logistic regression algorithms were used to construct the models. The HE image features were used to build the HE model, the Ki67 image features were used for the Ki67 model, and the combined model included features from both the HE and Ki67 images, as well as tumor markers (CEA, CA724, CA125, and CA242). The predictive results of the HE model, Ki67 model, and tumor markers were visualized through a nomogram. The models were evaluated using ROC curve analysis, and their clinical value was estimated using decision curve analysis (DCA). Results A total of 260 deep learning features were extracted from HE or Ki67 images. The AUC for the HE model and Ki67 model in the training cohort was 0.885 and 0.890, and in the testing cohort, it was 0.703 and 0.767, respectively. The combined model and nomogram in the training cohort had AUC values of 0.907 and 0.926, and in the testing cohort, they had AUC values of 0.814 and 0.817. In clinical DCA, the net benefit of the Ki67 model was superior to the HE model. The combined model and nomogram showed significantly higher net benefits compared to the individual HE model or Ki67 model. Conclusion The combined model and nomogram, which integrate pathomics multi‐modal data and clinical‐pathological variables, demonstrated superior performance in distinguishing between Stage I–II and Stage III colorectal cancer. This provides valuable support for clinical decision‐making and may improve treatment strategies and patient prognosis. Furthermore, the use of immunohistochemistry (Ki67) slides for pathomics modeling outperformed HE slide, offering new insights for future pathomics research.

Background: The success of falls prevention exercise intervention is dependent on adherence. This study investigated the trajectories of adherence to an instructor-led, face-to-face, community group-based falls-prevention exercise intervention, baseline factors associated with these trajectories, and the impact of these adherence trajectories on post-intervention physical functioning. Qualitative interviews were also examined to gather insights into participants’ profiles. Methods: Attendance data from 144 older adults who participated in a 12-week falls prevention exercise intervention were used to assess adherence. Physical functioning was assessed at baseline and post-intervention using Short Physical Performance Battery. We used Latent Class Growth Modelling to identify the types of adherence trajectories. Multinomial logistic regression and multivariate linear regression were used to examine antecedents and consequences associated with the adherence trajectories. Content and cross-tabulation analysis were conducted on 30 semi-structured qualitative interviews. Results: We identified four types of adherence trajectories: high adherence, moderate adherence, low adherence, and non-adherence. Non-adherent participants were younger than those with a high adherence trajectory. Those with low adherence trajectory were younger, more likely to be an ethnic minority, and had lower exercise self-efficacy. Older adults with low or non-adherence trajectories had lower post-intervention physical functioning than those with high adherence trajectory. Qualitative analysis indicated that participants with low adherence trajectory had prioritized exercise lower than other life commitments and had frequent hospitalizations and multiple medical appointments. Conclusion: Non and low adherence trajectories attenuate the impact of falls prevention exercise intervention. Findings from this study can be used to develop strategies to improve adherence.

ABSTRACT: Duck adenovirus type-3 (DAdV-3) is a poorly characterized duck virus. A comprehensive analysis of the DAdV-3 pathogenicity and host immune response could be a valuable addition. Herein, DAdV-3 was isolated from Muscovy duck and virus-specific genes were confirmed by polymerase chain reaction (PCR). The obtained gene fragments were sequenced and compared with the reference sequence. Results confirmed that the clinically isolated virus was DAdV-3, named as HF-AN-2020. To evaluate DAdV-3 host immune response, the expression levels of MDA5, STING, IRF7, MAVS, and NF-κB, and inflammatory cytokines (IFN-β, IFN-γ, and IL-1β) were determined by quantitative reverse transcriptase PCR (qRT-PCR). The expression levels of IFN-β and IFN-γ were 32.6- and 28.6-fold, respectively, higher (P < 0.01) than the control group. It was found that the upregulation of STING and NF-κB pathways was directly involved in the regulation of inflammatory cytokines (IFN-β, IFN-γ, and IL-1β). Furthermore, the gene regulation pathways consecutively upregulated the expression levels of MDA5, STING, IRF7, MAVS, and NF-κB up to 31.6, 10.5, 31.4, 2.2, and 2.6-fold, respectively, higher (P < 0.01) than the control group. The TCID50 of DAdV-3 for Muscovy duck and chicken was 10−3.24/0.1 mL with 0% mortality, indicating low pathogenicity in both Muscovy ducks and chickens, but DAdV-3 can induce higher expression of interferons. Genome analysis showed mutations in 4 amino acids located in ORF19B (Ser to Thr), ORF66 (Leu to Phe, Ile to Leu), and ORF67 (Gly to stop codon). This study provides essential and basic information for further research on the mechanism of the cellular immune responses against adenoviruses.

Summary: The pandemic HIV-1, HIV-1 group M, emerged from a single spillover event of its ancestral lentivirus from a chimpanzee. During human-to-human spread worldwide, HIV-1 diversified into multiple subtypes. Here, our interdisciplinary investigation mainly sheds light on the evolutionary scenario of the viral budding system of HIV-1 subtype C (HIV-1C), a most successfully spread subtype. Of the two amino acid motifs for HIV-1 budding, the P(T/S)AP and YPxL motifs, HIV-1C loses the YPxL motif. Our data imply that HIV-1C might lose this motif to evade immune pressure. Additionally, the P(T/S)AP motif is duplicated dependently of the level of HIV-1 spread in the human population, and >20% of HIV-1C harbored the duplicated P(T/S)AP motif. We further show that the duplication of the P(T/S)AP motif is caused by the expansion of the CTG triplet repeat. Altogether, our results suggest that HIV-1 has experienced a two-step evolution of the viral budding process during human-to-human spread worldwide.

Objective: This study aims to develop and validate an innovative radiopathomics model that combines radiomics and pathomics features to effectively differentiate between stages I-II and stage III gastric cancer (pathological staging). Methods: Our study included 200 patients with well-defined stages of gastric cancer divided into a training cohort (n = 140) and a test cohort (n = 60). Radiomics features were extracted from contrast-enhanced CT images using PyRadiomics, while pathomics features were obtained from whole slide images of pathological specimens through a fine-tuned deep learning model (ResNet-18). After rigorous feature dimensionality reduction and selection, we constructed radiomics models (SVM_rad, LR_rad, and MLP_rad) and pathomics models (SVM_path, LR_path, and MLP_path) utilizing support vector machine (SVM), logistic regression (LR), and multilayer perceptron (MLP) algorithms. The optimal radiomics and pathomics models were chosen based on comprehensive evaluation criteria such as ROC curves, Hosmer‒Lemeshow tests, and calibration curve tests. Feature patterns extracted from the best-performing radiomics model (MLP_rad) and pathomics model (SVM_rad) were integrated to create a powerful radiopathomics nomogram. Results: From a pool of 1834 radiomics features extracted from CT images, 14 were selected to construct radiomics models. Among these, the MLP_rad model exhibited the most robust predictive performance (AUC, training cohort: 0.843; test cohort: 0.797). Likewise, 10 pathomics features were chosen from 512 extracted from whole slide images to build pathomics models, with the SVM_path model demonstrating the highest predictive efficiency (AUC, training cohort: 0.937; test cohort: 0.792). The combined radiopathomics nomogram model exhibited optimal discriminative ability (AUC, training cohort: 0.951; test cohort: 0.837), as confirmed by decision curve analysis (DCA), which indicated superior clinical effectiveness. Conclusion: This study presents a cutting-edge radiopathomics nomogram model designed to predict pathological staging in gastric cancer, distinguishing between stages I-II and stage III. Our research leverages preoperative CT images and histopathological slides to forecast gastric cancer staging accurately, potentially facilitating the estimation of staging before radical gastric cancer surgery in the future.

Abstract Background There has been ongoing debate about the use of tourniquets in total knee arthroplasty, and their application is widely studied. A comprehensive understanding of the advantages and disadvantages of tourniquet use during the procedure is crucial for optimizing surgical outcomes. This study aimed to investigate the effectiveness of tourniquet application, with a particular focus on blood loss and perioperative complications, providing valuable insights for clinical practice. Methods Fifty patients who underwent total knee arthroplasty were randomized into tourniquet (n = 25) and nontourniquet (n = 25) groups. The same surgeon performed all surgical procedures. The follow-up time was 14 days after surgery. Primary outcomes were hemoglobin level changes, blood loss, operation time, and perioperative plasma D-dimer levels. Secondary outcomes were postoperative complications, including thrombotic and nonthrombotic events. Results No significant differences were found in drainage, calculated blood loss, total blood loss, postoperative hemoglobin levels, or blood transfusion between the two groups (P > 0.05). No differences in D-dimer levels were observed on postoperative Days 1, 3, and 14 between the two groups, except on postoperative Day 7, when the D-dimer level in the tourniquet group was lower than that in the nontourniquet group (P = 0.03). The incidence of local complications (thigh bruising, blisters, pain, fat liquefaction, and superficial infections) in the tourniquet group was significantly higher than that in the nontourniquet group (P = 0.03), but no significant differences were found in thromboembolic and nonthromboembolic events or overall complications (P > 0.05). Conclusion We conclude that tourniquet use does not reduce the length of surgery or blood loss but does increase local complications in total knee arthroplasty.

Water security and urban flooding have become major sustainability issues. This paper presents a novel method to introduce rates of change as the state-of-the-art approach in artificial intelligence model development for sustainability agenda. Multi-layer perceptron (MLP) and deep learning long short-term memory (LSTM) models were considered for flood forecasting. Historical rainfall data from 2008 to 2021 at 11 telemetry stations were obtained to predict flow at the confluence between Klang River and Ampang River. The initial results of MLP yielded poor performance beneath normal expectations, which was R = 0.4465, MAE = 3.7135, NSE = 0.1994 and RMSE = 8.8556. Meanwhile, the LSTM model generated a 45% improvement in its R-value up to 0.9055. Detailed investigations found that the redundancy of data input that yielded multiple target values had distorted the model performance. Qt was introduced into input parameters to solve this issue, while Qt+0.5 was the target value. A significant improvement in the results was detected with R = 0.9359, MAE = 0.7722, NSE = 0.8756 and RMSE = 3.4911. When the rates of change were employed, an impressive improvement was seen for the plot of actual vs. forecasted flow. Findings showed that the rates of change could reduce forecast errors and were helpful as an additional layer of early flood detection. HIGHLIGHTS A highly accurate flood forecasting system based on deep learning has been developed.; Multi-lead ahead forecasting for streamflow has been investigated.; The novel architecture model has been successfully applied for controlling streamflow in SMART tunnel.; The proposed new model architecture could be applied to forecast river streamflow in different hydrological areas;

Abstract Whispering-gallery-mode microcavity lasers possess remarkable characteristics such as high Q factors and compact geometries, making them an essential element in the evolution of microlasers. However, solid-state whispering-gallery-mode lasers have previously suffered from low output power and limited optical conversion efficiency, hindering their applications. Here, we present the achievement of milliwatt laser emissions at a wavelength of 1.06 µm from a solid-state whispering-gallery-mode laser. To accomplish this, we construct a whispering-gallery-mode microcavity (with a diameter of 30 µm) using a crystalline Nd: YAG thin film obtained through carbon-implantation enhanced etching of a Nd: YAG crystal. This microcavity laser demonstrates a maximum output power of 1.12 mW and an optical conversion efficiency of 12.4%. Moreover, our unique eccentric microcavity design enables efficient coupling of free-space pump light, facilitating integration with a waveguide. This integration allowed for single-wavelength laser emission from the waveguide, achieving an output power of 0.5 mW and an optical conversion efficiency of 6.18%. Our work opens up new possibilities for advancing solid-state whispering-gallery-mode lasers, providing a viable option for compact photonic sources.

The dominant spoilage bacteria in chicken feet with pickled peppers were analyzed by high-throughput sequencing technology and isolated by the traditional culture method to evaluate their spoilage capacity by back inoculation. The results showed that the dominant genus identified was Bacillus, and four dominant strains were identified including B. methylotrophicus, B. velezensis, B. subtilis and B. safensis. All these strains were able to produce protease and lipase activity. Among them, B. safensis showed the strongest protease activity (51.19 U/mL), while B. methylotrophicus showed the strongest lipase activity (3.75 U/mL). The pH, total volatile basic nitrogen (TVB-N) content and thiobarbituric acid reactive substances (TBARS) value of the samples inoculated with each of the four Bacillus strains were higher than those of the uninoculated control group, indicating that all four Bacillus strains had spoilage capacity. This study will provided a theoretical basis for preventing and controlling the spoilage of chicken feet with pickled peppers and extending its shelf life.

Abstract Urbanization is a trend lasting for more than one century worldwide. Four hundred ninety male and female adult Chinese Han participants with different urban and rural childhoods were included in this study. Early-life urban environment was found benefit for total grey matter volume (GMV), dorsolateral prefrontal cortex (DLPFC) GMV, temporal pole (TP) GMV and cognition function, and negatively correlated with medial prefrontal cortex (MPFC) GMV. Regression analysis showed that maternal education was a protective factor for total and DLPFC GMVs, while having siblings was better for MPFC GMV. Total, DLPFC and TP GMVs acts mediation effects between childhood urbanicity and different cognitive domains. These findings may suggest some pros and cons on brain structure associated with childhood urbanicity and related environmental factors.