Objective: To identify the structure of the complementarity determining region (CDRs), the V(D)J rearrangement and somatic hypermutational characteristics of the heavy and light chains of a red blood cell blood group-specific monoclonal antibody. Methods: The hybridoma cell line secreting human IgM κ monoclonal anti-P antibody was used as the research object. Total RNA was extracted from cultured monoclonal cell line, and cDNA was obtained by reverse transcription PCR (RT-PCR) using random hexamers primers. It was then amplified and sequenced using primers specific for variable regions of the immunoglobulin heavy and light chains encoding the anti-P antibody. The sequences were aligned against the NCBI database using online Immunoglobulin BLAST (Ig-BLAST) tool. Results: The study determined the structure of the CDRs and framework regions (FRs) of the variable regions of human monoclonal anti-P immunoglobulin, as well as the characteristics of V(D)J rearrangement. Moreover, the closest VH, VD, and VJ germline alleles for the heavy chain and VL and VJ germline alleles for the light chain were also identified. The IgH gene rearrangment pattern of the monoclonal anti-P was IGHV6-1 * 01—IGHD5-18 02—IGHJ4 02 and IgL gene was IGκV1-12 01—IGκJ3 01. Nine base mutations occurred within the germline gene IGHV6-1 01 in variable region of heavy chain, whereas 5 base mutations were found in the germline gene IGκV1-12 01 in variable region of light chain, respectively. Conclusion: This study characterized the CDR structure in monoclonal antibody cell line targeting the high-frequency red blood cell P antigen, and provided a foundation for the construction of recombinant antibody expressing plasmids and transfomation of the immunoglobulin type.

Objective To identify factors affecting and key environmental factors of the Oncomelania hupensis snail density in the Yangtze River Delta region using machine learning methods. Methods Administrative village-level O. hupensis snail survey data in the Yangtze River Delta (including Shanghai Municipality, Jiangsu Province, Zhejiang Province and Anhui Province) from 2011 to 2021 were retrieved from the Information Management System for Parasitic Disease Control of Chinese Center for Disease Control and Prevention. Environmental factor data were captured from the Google Earth Engine platform, including elevation, slope, terrain, normalized difference vegetation index (NDVI), vegetation type, soil type, total petroleum hydrocarbon (TPH), ammonium nitrogen, inorganic nitrogen, dissolved oxygen, pH of water, chemical oxygen demand (COD) and inorganic phosphorus, and climatic factor data in the study region were retrieved from the Copernicus Climate Data Store, including annual precipitation, aridity index and annual mean temperature (AMT). O. hupensis snail survey data in the Yangtze River Delta region from 2011 to 2021 were randomly divided into a training set (70%) and a test set (30%), and five machine learning models were selected for machine learning model construction and comparative analysis of the O. hupensis snail density using the software R 4.3.0, including random forest (RF), eXtreme gradient boosting (XGBoost), support vector machine (SVM), gradient boosting machine (GBM) and neural network (NN). The XGBoost model was employed to construct a predictive model for the O. hupensis snail density, and the impact of each environmental factor on O. hupensis snail distribution was quantified. The SHapley Additive exPlanations (SHAPs) values were calculated to estimate the average contribution of each variable to the model prediction, and the core environmental factors affecting the O. hupensis snail population density were screened. Results Among the five machine learning models, the XGBoost model exhibited the optimal comprehensive performance, with the coefficient of determination (R²) of 0.855, mean squared error (MSE) of 0.188, root mean squared error (RMSE) of 0.434 and mean absolute error (MAE) of 0.155, respectively. Analysis of factors affecting the O. hupensis snail density with the XGBoost model showed that among the 16 environmental factors, the top four high-impact factors ranked by SHAPs values included annual precipitation, elevation, aridity index and NDVI, with cumulative SHAPs contributions of 75%, which was higher than that of other environmental factors. If NDVI was higher than 0.6, the O. hupensis snail density increased with NDVI and peaked if NDVI was 0.8 (1.60 snails/0.1 m²). The O. hupensis snail density increased with elevation if the elevation ranged from 14 to 40 m, and slowly rose if the annual precipitation ranged from 900 to 1 300 mm, and then increased rapidly to the peak (1.52 snails/0.1 m²) if the annual precipitation ranged from 1 300 to 1 500 mm. In addition, the O. hupensis snail density increased rapidly to the maximum (1.60 snails/0.1 m²) if the aridity index ranged from 0.8 to 1.1, and decreased gradually if the aridity index exceeded 1.1. Conclusions The XGBoost model shows excellent performance in prediction of the O. hupensis snail density and identification of key environmental factors in the Yangtze River Delta region. Annual precipitation, elevation, aridity index and NDVI are key environmental factors affecting the distribution and density of O. hupensis snails in the Yangtze River Delta region.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

OBJECTIVE:Based on different algorithms of machine learning,the prediction model of lumbar disc herniation has become a trend and hot spot in the development of precision medicine.However,there is limited evidence on the reporting quality and methodological quality of prediction models of lumbar disc herniation outcomes using machine learning.This article is aimed to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation by comprehensively analyzing the report quality and risk of bias of previous studies that developed and validated prognosis prediction models based on machine learning through a comprehensive literature search,in order to explore the performance of machine learning algorithms in predicting the prognosis of lumbar disc herniation.METHODS:The databases of CNKI,WanFang,VIP,SinOMED,PubMed,Web of Science,Embase,and The Cochrane Library were searched by computer.Studies on the use of machine learning to develop(and/or validate)prognostic prediction models for lumbar disc herniation were collected from the inception of the database to December 31,2023.Two researchers independently screened the literature,extracted data,and assessed the risk of bias of the included studies.The reporting quality and risk of bias of the included studies were assessed by the Multivariable Transparent Reporting of Predictive Models(TRIPOD)statement and the Predictive Model Risk of Bias Assessment Tool(PROBAST).The results of the evaluation were analyzed using descriptive statistics and visual charts.RESULTS:(1)A total of 23 articles were included,and the TRIPOD compliance of each study ranged from 11％to 87％,with a median compliance of 54％.The quality of reporting of titles,detailed descriptions of treatment measures,blinding of predictors,handling of missing data,details of risk stratification,specific procedures for enrollment,model interpretation,and model performance was mostly poor,with TRIPOD adherence rates ranging from 4％to 35％.(2)Of all included studies,61％had a high risk of bias and 39％had an unclear overall risk of bias.The area under the curve,accuracy,sensitivity and specificity were used to evaluate the performance of the model.The areas under the curve of 20 models were reported,ranging from 0.561 to 0.999.Three models reported the accuracy of the model,ranging from 82.07％to 89.65％.(3)Among all included studies,the statistical analysis domain was most often assessed as having a high risk of bias,mainly due to the small number of valid samples,the selection of predictors based on univariate analysis and the lack of calibration and discrimination assessment of the model in the study.CONCLUSION:These results indicate that machine learning can achieve good predictive ability in the development and validation of prognostic models for lumbar disc herniation.The commonly used algorithms include regression algorithm,support vector machine,decision tree,random forest,artificial neural network,naive Bayes and other algorithms.Reasonable algorithms combined with clinical practice can improve the accuracy of prognosis prediction of lumbar disc herniation.However,the reporting and methodological quality of prognosis prediction models based on machine learning are poor,the prediction performance of different models varies greatly,and the generalization and extrapolation of research models are unclear.There is an urgent need to improve the design,implementation and reporting of such studies.To promote the application of machine learning in the clinical practice of lumbar disc herniation prediction models,it is necessary to comprehensively consider various predictors related to the prognosis of the disease before modeling,and strictly follow the relevant standards of PROBAST tool during modeling.

Effective symptom management can alleviate the physical and psychological distress experienced by patients with cancer, improve quality of life, and contribute to treatment adherence and improve clinical outcomes. However, most existing guidelines are developed for healthcare professionals, and patients and the public have limited access to standardized and comprehensible guidance on symptom management. To address this gap and to facilitate effective communication and shared decision-making, this study proposes the development of a patient version of the cancer symptom management guideline. The development process will adhere to the methodological framework recommended by the Guidelines International Network and the World Health Organization. The GRADE approach will be employed to assess the certainty of evidence and to formulate recommendations. In addition, the process will be informed by the Appraisal of Guidelines for Research and Evaluation Ⅱ (AGREE Ⅱ) instrument and the Reporting Items for Practice Guidelines in Healthcare-Public or Patient Versions of Guidelines (RIGHT-PVG). This protocol outlines the establishment of the guideline working group, the identification and prioritization of key questions, evidence retrieval and appraisal, and the formulation of recommendations, with the aim of ensuring methodological rigor and transparency in the development of the patient guideline and providing methodological reference for similar guideline initiatives.

Schistosomiasis is prevalent in 78 countries and territories worldwide, while the eastern and western parts of sub-Sahara Africa bear the highest disease burden due to schistosomiasis. Recently, climate change, international trade and travel, urbanization and war have increased the risk of cross-boundary importation and transmission of schistosomiasis, and schistosomiasis has increasingly become a public health concern in non-endemic countries and territories. Biomphalaria straminea, the intermediate host of Schistosoma mansoni, has colonized in southern China and its habitats continue to move northward. In addition, cross-boundary imported cases of schistosomiasis have been reported occasionally in China. However, the real number of cases may be underestimated greatly due to insufficient diagnostic capacity and weak awareness of case reporting for overseas imported schistosomiasis in healthcare facilities. It is necessary to establish a multi-party collaborative mechanism, improve corresponding systems and technical specifications, reinforce surveillance and early warning, and border management, enhance technical reserves and capability building, and improve the awareness of schistosomiasis prevention and healthcare-seeking among entry-exit personnel, in order to effectively address the threat of cross-boundary imported schistosomiasis.

Hearing loss is the most prevalent disabling disease. Cochlear implantation（CI） serves as the primary intervention for severe to profound hearing loss. This consensus systematically explores the value of genetic diagnosis in the pre-operative assessment and efficacy prognosis for CI. Drawing upon domestic and international research and clinical experience, it proposes an evidence-based medicine three-tiered prognostic classification system（Favorable, Marginal, Poor）. The consensus focuses on common hereditary non-syndromic hearing loss（such as that caused by mutations in genes like GJB2, SLC26A4, OTOF, LOXHD1） and syndromic hereditary hearing loss（such as Jervell & Lange-Nielsen syndrome and Waardenburg syndrome）, which are closely associated with congenital hearing loss, analyzing the impact of their pathological mechanisms on CI outcomes. The consensus provides recommendations based on multiple round of expert discussion and voting. It emphasizes that genetic diagnosis can optimize patient selection, predict prognosis, guide post-operative rehabilitation, offer stratified management strategies for patients with different genotypes, and advance the application of precision medicine in the field of CI.
Humans ; Cochlear Implantation ; Prognosis ; Hearing Loss/surgery* ; Consensus ; Connexin 26 ; Mutation ; Sulfate Transporters ; Connexins/genetics*

Inflammatory bowel disease (IBD) is a chronic and recurrent intestinal disease, and has become a major global health issue. Individuals with IBD face an elevated risk of developing colorectal cancer (CRC), and recent studies have indicated that mitochondrial dysfunction plays a pivotal role in the pathogenesis of both IBD and CRC. This review covers the pathogenesis of IBD and CRC, focusing on mitochondrial dysfunction, and explores pharmacological targets and strategies for addressing both conditions by modulating mitochondrial function. Additionally, recent advancements in the pharmacological modulation of mitochondrial dysfunction for treating IBD and CRC, encompassing mitochondrial damage, release of mitochondrial DNA (mtDNA), and impairment of mitophagy, are thoroughly summarized. The review also provides a systematic overview of natural compounds (such as flavonoids, alkaloids, and diterpenoids), Chinese medicines, and intestinal microbiota, which can alleviate IBD and attenuate the progression of CRC by modulating mitochondrial function. In the future, it will be imperative to develop more practical methodologies for real-time monitoring and accurate detection of mitochondrial function, which will greatly aid scientists in identifying more effective agents for treating IBD and CRC through modulation of mitochondrial function.

OBJECTIVE: To compare the efficacy and safety of intravenous colistin sulfate combined with nebulized inhalation versus intravenous monotherapy for pulmonary infections caused by carbapenem-resistant organism (CRO). METHODS: A multicenter retrospective cohort study was conducted. Clinical data were collected from patients admitted to the intensive care unit (ICU) of 10 tertiary class-A hospitals in Henan Province between July 2021 and May 2023, who received colistin sulfate for CRO pulmonary infections. Data included baseline characteristics, inflammatory markers [white blood cell count (WBC), neutrophil count (NEU), procalcitonin (PCT), C-reactive protein (CRP)], renal function indicators [serum creatinine (SCr), blood urea nitrogen (BUN)], life support measures, anti-infection regimens, clinical efficacy, microbiological clearance rate, and prognostic outcomes. Patients were divided into two groups: intravenous group (colistin sulfate monotherapy via intravenous infusion) and combination group ((intravenous infusion combined with nebulized inhalation of colistin sulfate). Changes in parameters before and after treatment were analyzed. RESULTS: A total of 137 patients with CRO pulmonary infections were enrolled, including 89 in the intravenous group and 48 in the combination group. Baseline characteristics, life support measures, daily colistin dose, and combination regimens (most commonly colistin sulfate plus carbapenems in both groups) showed no significant differences between two groups. The combination group exhibited higher clinical efficacy [77.1% (37/48) vs. 59.6% (52/89)] and microbiological clearance rate [60.4% (29/48) vs. 39.3% (35/89)], both P < 0.05. Pre-treatment inflammatory and renal parameters showed no significant differences between two groups. Post-treatment, the combination group showed significantly lower WBC and CRP [WBC (×10⁹/L): 8.2±0.5 vs. 10.9±0.6, CRP (mg/L): 14.0 (5.7, 26.6) vs. 52.1 (24.4, 109.6), both P < 0.05], whereas NEU, PCT, SCr, and BUN levels showed no significant between two groups. ICU length of stay was shorter in the combination group [days: 16 (10, 25) vs. 21 (14, 29), P < 0.05], although mechanical ventilation duration and total hospitalization showed no significant differences between two groups. CONCLUSIONS Intravenous colistin sulfate combined with nebulized inhalation improved clinical efficacy and microbiological clearance in CRO pulmonary infections with an acceptable safety profile.
Humans ; Colistin/therapeutic use* ; Retrospective Studies ; Administration, Inhalation ; Anti-Bacterial Agents/therapeutic use* ; Carbapenems/pharmacology* ; Male ; Female ; Middle Aged ; Gram-Negative Bacteria/drug effects* ; Aged ; Treatment Outcome ; Respiratory Tract Infections/drug therapy*