Cross-site validation of lung cancer diagnosis by electronic nose with deep learning: a multicenter prospective study.

BACKGROUND: Although electronic nose (eNose) has been intensively investigated for diagnosing lung cancer, cross-site validation remains a major obstacle to be overcome and no studies have yet been performed. METHODS: Patients with lung cancer, as well as healthy control and diseased control groups, were prospectively recruited from two referral centers between 2019 and 2022. Deep learning models for detecting lung cancer with eNose breathprint were developed using training cohort from one site and then tested on cohort from the other site. Semi-Supervised Domain-Generalized (Semi-DG) Augmentation (SDA) and Noise-Shift Augmentation (NSA) methods with or without fine-tuning was applied to improve performance. RESULTS: In this study, 231 participants were enrolled, comprising a training/validation cohort of 168 individuals (90 with lung cancer, 16 healthy controls, and 62 diseased controls) and a test cohort of 63 individuals (28 with lung cancer, 10 healthy controls, and 25 diseased controls). The model has satisfactory results in the validation cohort from the same hospital while directly applying the trained model to the test cohort yielded suboptimal results (AUC, 0.61, 95% CI: 0.47─0.76). The performance improved after applying data augmentation methods in the training cohort (SDA, AUC: 0.89 [0.81─0.97]; NSA, AUC:0.90 [0.89─1.00]). Additionally, after applying fine-tuning methods, the performance further improved (SDA plus fine-tuning, AUC:0.95 [0.89─1.00]; NSA plus fine-tuning, AUC:0.95 [0.90─1.00]). CONCLUSION: Our study revealed that deep learning models developed for eNose breathprint can achieve cross-site validation with data augmentation and fine-tuning. Accordingly, eNose breathprints emerge as a convenient, non-invasive, and potentially generalizable solution for lung cancer detection. CLINICAL TRIAL REGISTRATION: This study is not a clinical trial and was therefore not registered.

Sputum bacterial microbiota signature as a surrogate for predicting disease progression of nontuberculous mycobacterial lung disease.

OBJECTIVES: Predicting progression of nontuberculous mycobacterial lung disease (NTM-LD) remains challenging. This study evaluated whether sputum bacterial microbiome diversity can be the biomarker and provide novel insights into related phenotypes and treatment timing. METHODS: We analyzed 126 sputum microbiomes of 126 patients with newly diagnosed NTM-LD due to Mycobacterium avium complex, M. abscessus complex, and M. kansasii between May 2020 and December 2021. Patients were followed for 2 years to determine their disease progression status. We identified consistently representative genera that differentiated the progressor and nonprogressor by using six methodologies. These genera were used to construct a prediction model using random forest with 5-fold cross validation. RESULTS: Disease progression occurred in 49 (38.6%) patients. Compared with nonprogressors, α-diversity was lower in the progressors. Significant compositional differences existed in the ß-diversity between groups (p=0.001). The prediction model for NTM-LD progression constructed using seven genera (Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phascolarctobacterium) with significantly differential abundance achieved an area under curve of 0.871. CONCLUSIONS: Identification of the composition of sputum bacterial microbiome facilitates prediction of the course of NTM-LD, and maybe used to develop precision treatment involving modulating the respiratory microbiome composition to ameliorate NTM-LD.

A study on expression recognition based on improved mobilenetV2 network.

This paper proposes an improved strategy for the MobileNetV2 neural network(I-MobileNetV2) in response to problems such as large parameter quantities in existing deep convolutional neural networks and the shortcomings of the lightweight neural network MobileNetV2 such as easy loss of feature information, poor real-time performance, and low accuracy rate in facial emotion recognition tasks. The network inherits the characteristics of MobilenetV2 depthwise separated convolution, signifying a reduction in computational load while maintaining a lightweight profile. It utilizes a reverse fusion mechanism to retain negative features, which makes the information less likely to be lost. The SELU activation function is used to replace the RELU6 activation function to avoid gradient vanishing. Meanwhile, to improve the feature recognition capability, the channel attention mechanism (Squeeze-and-Excitation Networks (SE-Net)) is integrated into the MobilenetV2 network. Experiments conducted on the facial expression datasets FER2013 and CK + showed that the proposed network model achieved facial expression recognition accuracies of 68.62% and 95.96%, improving upon the MobileNetV2 model by 0.72% and 6.14% respectively, and the parameter count decreased by 83.8%. These results empirically verify the effectiveness of the improvements made to the network model.

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: µECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

Protocol for quantitative evaluation of misfolded protein degradation using Agrobacterium-mediated expression system in Nicotiana benthamiana.

Cellular protein homeostasis is maintained by the disposal of aggregated misfolded proteins. Here, we present a protocol for investigating the involvement of the proteins of interest in misfolded protein degradation via Agrobacterium-mediated transient expression in Nicotiana benthamiana. We describe in detail the steps of misfolded protein design, transient protein expression in N. benthamiana, subsequent total protein extraction, and quantification of misfolded proteins through western blotting. This generalizable system can be used for misfolded proteins derived from various plants or microbes. For complete details on the use and execution of this protocol, please refer to Ai et al.1.

Prevalence of depressive symptoms in patients with advanced schistosomiasis in China: A systematic review and meta-analysis.

BACKGROUND: Advanced schistosomiasis is the most serious outcome of infection and has a negative impact on both physical fitness and mental health of patients, the latter of which has long been overlooked. Therefore, we performed this systematic review and meta-analysis to estimate the overall prevalence of depressive symptoms, one of the most common mental problems, in patients with advanced schistosomiasis in China. METHODS: Six electronic databases were searched for studies reporting the prevalence of depressive symptoms in the targeted patients. Assessments were pooled using a fixed- or random-effects model based on heterogeneity test. Subgroup analyses were further performed and differences between/among groups were examined using the chi-squared test. The protocol had previously been registered in PROSPERO (CRD42023406708). RESULTS: A total of 11 studies with 1,673 participants were included. The pooled prevalence of depressive symptoms in advanced schistosomiasis in China was 62.01% (95% CI: 51.30% - 72.72%), with a significant heterogeneity among studies. Depressive symptoms were more prevalent in patients with complications and more than half of the patients suffered a mild- or moderate-level of depression. No publication bias was found, and sensitivity analysis showed a stable result. CONCLUSIONS: The overall prevalence of depressive symptoms in advanced schistosomiasis in China was high enough to warrant psychotherapeutic interventions, especially for patients with complications. This would greatly prevent or/and reduce depression and improve their quality of life.

Single-cell and multi-omics analyses highlight cancer-associated fibroblasts-induced immune evasion and epithelial mesenchymal transition for smoking bladder cancer.

Tobacco carcinogens are recognized as critical hazard factors for bladder tumorigenesis, affecting the prognosis of patients through aromatic amines components. However, the specific function of tobacco carcinogens and systematic assessment models in the prognosis of bladder cancer remains poorly elucidated. We retrieved bladder cancer specific tobacco carcinogens-related genes from Comparative Toxicogenomic Database, our Nanjing Bladder Cancer cohort and TCGA database. Gene×Gene interaction method was utilized to establish a prognostic signature. Integrative assessment of immunogenomics, tumor microenvironments and single-cell RNA-sequencing were performed to illustrate the internal relations of key events from different levels. Finally, we comprehensively identified 33 essential tobacco carcinogens-related genes to construct a novel prognostic signature, and found that high-risk patients were characterized by significantly worse overall survival (HR=2.25; Plog-rank < 0.01). Single-cell RNA-sequencing and multi-omics analysis demonstrated that cancer-associated fibroblasts mediated the crosstalk between epithelial-mesenchymal transition progression and immune evasion. Moreover, an adverse outcome pathway framework was established to facilitate our understanding to the tobacco carcinogens-triggered bladder tumorigenesis. Our study systematically provided immune microenvironmental alternations for smoking-induced adverse survival outcomes in bladder cancer. These findings facilitated the integrative multi-omics insights into risk assessment and toxic mechanisms of tobacco carcinogens.

Computer-Vision-Based Sensing Technologies for Livestock Body Dimension Measurement: A Survey.

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock.

Deep Learning-based Diagnosis of Pulmonary Tuberculosis on Chest X-ray in the Emergency Department: A Retrospective Study.

Prompt and correct detection of pulmonary tuberculosis (PTB) is critical in preventing its spread. We aimed to develop a deep learning-based algorithm for detecting PTB on chest X-ray (CXRs) in the emergency department. This retrospective study included 3498 CXRs acquired from the National Taiwan University Hospital (NTUH). The images were chronologically split into a training dataset, NTUH-1519 (images acquired during the years 2015 to 2019; n = 2144), and a testing dataset, NTUH-20 (images acquired during the year 2020; n = 1354). Public databases, including the NIH ChestX-ray14 dataset (model training; 112,120 images), Montgomery County (model testing; 138 images), and Shenzhen (model testing; 662 images), were also used in model development. EfficientNetV2 was the basic architecture of the algorithm. Images from ChestX-ray14 were employed for pseudo-labelling to perform semi-supervised learning. The algorithm demonstrated excellent performance in detecting PTB (area under the receiver operating characteristic curve [AUC] 0.878, 95% confidence interval [CI] 0.854-0.900) in NTUH-20. The algorithm showed significantly better performance in posterior-anterior (PA) CXR (AUC 0.940, 95% CI 0.912-0.965, p-value < 0.001) compared with anterior-posterior (AUC 0.782, 95% CI 0.644-0.897) or portable anterior-posterior (AUC 0.869, 95% CI 0.814-0.918) CXR. The algorithm accurately detected cases of bacteriologically confirmed PTB (AUC 0.854, 95% CI 0.823-0.883). Finally, the algorithm tested favourably in Montgomery County (AUC 0.838, 95% CI 0.765-0.904) and Shenzhen (AUC 0.806, 95% CI 0.771-0.839). A deep learning-based algorithm could detect PTB on CXR with excellent performance, which may help shorten the interval between detection and airborne isolation for patients with PTB.

Effects of occupational exposure to dust, gas, vapor and fumes on chronic bronchitis and lung function.

Background: Chronic obstructive pulmonary disease (COPD) is one of the leading causes of mortality worldwide, and therefore the identification of the modifiable risk factors [such as exposure to vapors, gases, dust and fumes (VGDF)] for accelerate disease progression has important significance. Methods: We conducted COPD surveillance in six cities of southern China between 2014 and 2019. We recorded the diagnosis of chronic bronchitis, respiratory symptoms, occupational exposure to VGDF and other covariates by using a structured questionnaire. Logistic regression and multivariate linear regression model were adopted for analysis. We performed sensitivity analyses based on two methods of propensity score (PS) methods to evaluate the robustness of our results. Results: A total of 7,418 participants were included. Cough [odds ratios (ORs): 1.60, 95% confidence interval (CI): 1.22 to 2.08] and phlegm (OR: 1.49, 95% CI: 1.19 to 1.85) correlated significantly with exposure to dust. There was an increased risk of cough (OR: 1.53, 95% CI: 1.11 to 2.07) for occupational exposure to gas/vapor/fume. Dual exposure to dust and gas/vapor/fume was associated with a significantly increased risk of chronic bronchitis (OR: 1.74, 95% CI: 1.20 to 2.52), cough (OR: 1.43, 95% CI: 1.15 to 1.79) and phlegm (OR: 1.49, 95% CI: 1.24 to 1.79). In 5,249 participants with complete data of spirometry, gas/vapor/fume was associated with a decreased ratio of forced expiratory volume in one second and forced vital capacity (FEV1/FVC) (ß: -1.05, 95% CI: -1.85 to -0.26) and maximal mid-expiratory flow (MMEF) (ß: -0.15, 95% CI: -0.23 to -0.07). Dual exposure to dust and gas/vapor/fume was significantly associated with decreased FEV1/FVC (ß: -0.74, 95% CI: -1.28 to -0.20) and MMEF (ß: -0.06, 95% CI: -0.12 to -0.01). Results of sensitivity analysis were not materially changed. Conclusions: VGDF exposure is associated with chronic bronchitis, respiratory symptoms and decreased lung function, suggesting that VGDF contributes to the pathogenesis and progression of COPD.

Expert Consensus on the Diagnosis and Treatment of NRG1/2 Gene Fusion Solid Tumors.

The fusion genes NRG1 and NRG2 , members of the epidermal growth factor (EGF) receptor family, have emerged as key drivers in cancer. Upon fusion, NRG1 retains its EGF-like active domain, binds to the ERBB ligand family, and triggers intracellular signaling cascades, promoting uncontrolled cell proliferation. The incidence of NRG1 gene fusion varies across cancer types, with lung cancer being the most prevalent at 0.19 to 0.27%. CD74 and SLC3A2 are the most frequently observed fusion partners. RNA-based next-generation sequencing is the primary method for detecting NRG1 and NRG2 gene fusions, whereas pERBB3 immunohistochemistry can serve as a rapid prescreening tool for identifying NRG1 -positive patients. Currently, there are no approved targeted drugs for NRG1 and NRG2 . Common treatment approaches involve pan-ERBB inhibitors, small molecule inhibitors targeting ERBB2 or ERBB3, and monoclonal antibodies. Given the current landscape of NRG1 and NRG2 in solid tumors, a consensus among diagnostic and treatment experts is proposed, and clinical trials hold promise for benefiting more patients with NRG1 and NRG2 gene fusion solid tumors.

Quantifying the contribution of smear-negative, culture-positive pulmonary tuberculosis to nosocomial transmission.

BACKGROUND: Despite current guidelines for tuberculosis (TB) control in health care settings, which focused on smear-positive cases, prevention of nosocomial TB transmission continues to be a challenge. Here, we report the results of the first hospital-wide prospective study applying interferon-gamma release assay to investigate the role of smear-negative, culture-positive index cases in nosocomial TB transmission. METHODS: We prospectively identified cases of culture-confirmed smear-negative pulmonary TB receiving aerosol-generating procedures (AGPs) and cases of culture-confirmed smear-positive pulmonary TB admitted at a medical center. Nosocomial transmission was evaluated by screening their close contacts for latent TB infection (LTBI) using an interferon-gamma release assay. RESULTS: A total of 93 smear-negative index receiving AGP and 122 smear-positive index were enrolled. Among them, 13 (14.0%) and 43 (35.2%) index cases, respectively, had secondary cases of LTBI (P < .001). Sputum smear negativity (adjusted odds ratio: 0.20 [0.08-0.48]) and AGP (sputum suction; adjusted odds ratio: 3.48 [1.34-9.05]) are independent factors of transmission. A similar proportion in the close contacts of the 2 index groups had LTBI (17 [15.3%] and 63 [16.0%], respectively), and the former index group contributed to 21.3% of the nosocomial transmission. CONCLUSIONS: Smear-negative, culture-positive index cases receiving AGPs could be as infectious as smear-positive index cases. Hospital TB control policy should also focus on the former group.

Small cell conversion complicated with hypertrophic pulmonary osteoarthropathy after targeted therapy for advanced EGFR-mutated lung adenocarcinoma: A case report.

In recent years, with the opening of the era of precision therapy, the treatment of patients with positive driver genes is a hot issue in global research. EGFR is the most common driver gene in NSCLC, with a positivity rate of 17%. Although targeted drugs for EGFR mutations can benefit this population with efficacy, target therapy resistance inevitably occurs. The presented case suggests that a patient with advanced lung adenocarcinoma with EGFR mutation who developed pathological-type conversion of small cell lung cancer complicated with the development of hypertropic pulmonary osteoarthropathy (HPOA) after 6 months of targeted therapy. This case demonstrates that early diagnosis of HPOA can predict the occurrence of target resistance and pathologic conversion in patients with positive driver genes, providing new clues for the clinical management of lung cancer.

Fast needling combined with occupational therapy for hand dysfunction of spastic cerebral palsy : a randomized controlled trial. / å¿«é’ˆç»“åˆä½œä¸šç–—æ³•æ²»ç–—ç—‰æŒ›åž‹è„‘ç˜«æ‚£å„¿æ‰‹åŠŸèƒ½éšœç¢ï¼šéšæœºå¯¹ç §è¯•éªŒ.

OBJECTIVES: To compare the clinical effect of fast needling (without needle retaining), needle retaining acupuncture combined with occupational therapy and simple occupational therapy for hand dysfunction of spastic cerebral palsy in children. METHODS: A total of 75 children with spastic cerebral palsy were randomly divided into an occupational therapy group (25 cases), a fast needling group (25 cases, 1 case dropped out) and a needle retaining group (25 cases, 1 case dropped out). The patients in the occupational therapy group were only treated with occupational therapy for 20 min each time. The patients in the fast needling group were treated with acupuncture (without needle retaining) combined with occupational therapy, and the needle retaining group was treated with acupuncture (needle retaining) combined with occupational therapy, and acupuncture was taken at Hegu (LI 4), Houxi (SI 3), Yuji (LU 10), Waiguan (SJ 5), Jianyu (LI 15) and so on. The needles were retained for 30 min in patients of the needle retaining group. All the above treatments were performed once a day, 5 times a week for 12 weeks. The scores of fine motor function measure (FMFM) and Peabody developmental motor scale 2 (PDMS-2) were observed in patients of the three groups before and after treatment, and the safety of the fast needling group and the needle retaining group was compared. RESULTS: After treatment, the scores of FMFM and PDMS-2 in patients of the three groups were higher than those before treatment (P<0.01), and the scores of FMFM and PDMS-2 in the fast needling group and the needle retaining group were higher than those in the occupational therapy group (P<0.05, P<0.01). The incidence of acupuncture abnormalities in the fast needling group was 0.3% (5/1 440), which was lower than 1.4% (20/1 440) in the needle retaining group (P<0.05). CONCLUSIONS: Acupuncture combined with occupational therapy has better clinical effect than occupational therapy alone in improving hand dysfunction in children with spastic cerebral palsy, and there is no statistical difference in effect between fast needling acupuncture and needle retaining acupuncture, but fast needling has better safety than needle retaining acupuncture.

A phase Ib study evaluating the safety and efficacy of IBI310 plus sintilimab in patients with advanced non-small-cell lung cancer who have progressed after anti-PD-1/L1 therapy.

BACKGROUND: The development of immune checkpoint inhibitors has made a significant breakthrough in the treatment of non-small-cell lung cancer (NSCLC). However, there remains a huge unmet clinical need for patients with acquired resistance after initial treatment response. METHODS: This study evaluated the combination of IBI310 (an anti-cytotoxic T lymphocyte-associated antigen-4 [CTLA-4] antibody) and sintilimab (an anti-programmed death 1 [PD-1]) antibody) in NSCLC patients who have previously been treated with anti-PD-1/ligand (L)1 and acquired resistance. The patients were randomly assigned to receive either a lower dose of IBI310 (1 mg/kg Q3W, cohort A) or a higher dose of IBI310 (3 mg/kg Q3W, cohort B) in combination with sintilimab (200 mg Q3W). The primary endpoints of the study were objective response rate (ORR) assessed by RECISTv1.1 and safety, while secondary endpoints included disease control rate (DCR), progression-free survival (PFS), and overall survival (OS). RESULTS: As of November 2, 2023, the study had enrolled 30 patients, with 15 patients in each cohort. The ORR was 13.3% (2/15, 95% confidence interval [CI], 1.7-40.5) in cohort B. DCR were 46.7% (95% CI, 21.3-73.4) and 66.7% (95% CI, 38.4-88.2) in cohorts A and B, respectively. In cohorts A and B of this trial, the median follow-up times were 4.2 and 5.6 months, respectively. Median PFS was 1.45 (95% CI, 1.35-2.73) versus 2.73 (95% CI, 1.41-4.90) months for cohort A versus B; the median OS was 7.03 (95% CI, 3.09-not calculable [NC]) months in cohort A and 8.90 (95% CI, 5.13-NC) months in cohort B. Of the 30 patients, 86.7% in both cohorts experienced treatment-related adverse events (TRAEs) with Grade ≥3 TRAEs occurring in 40% and 53.3% of patients in cohorts A and B, respectively. CONCLUSION: IBI310 3 mg/kg Q3W plus sintilimab was effective in a small number of previously treated anti-PD-1/L1-resistant NSCLC patients.

Targeting FBXO22 enhances radiosensitivity in non-small cell lung cancer by inhibiting the FOXM1/Rad51 axis.

Radioresistance is a major constraint on the efficacy of lung cancer radiotherapy, but its mechanism has not been fully elucidated. Here, we found that FBXO22 was aberrantly highly expressed in lung cancer and that FBXO22 knockdown increased the radiosensitivity of lung cancer cells. Mechanistically, FBXO22 promoted Rad51 gene transcription by increasing the level of FOXM1 at the Rad51 promoter, thereby inducing the formation of lung cancer radioresistance. Furthermore, we found that deguelin, a potential inhibitor of FBXO22, enhanced radiosensitivity in an FBXO22/Rad51-dependent manner and was safely tolerated in vivo. Collectively, our results illustrate that FBXO22 induces lung cancer radioresistance by activating the FOXM1/Rad51 axis and provide preclinical evidence for the clinical translation of this critical target.

Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset.

BACKGROUND: Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health. METHODS: This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models-XG Boost, Support Vector Machine (SVM), and Random Forest (RF)-were employed. RESULTS: Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance. CONCLUSIONS: The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

Prevalence of and risk factors for surgical site infections after pancreaticoduodenectomy: a systematic review and meta-analysis.

Background: Surgical site infections (SSIs) are one of the most common complications after pancreaticoduodenectomy (PD); however, the global prevalence and risk factors for SSIs after PD remain unknown. Objectives: To investigate the prevalence of and risk factors for SSIs after PD. Methods: The PubMed, Embase, Cochrane Library, Web of Science, and Science Direct databases were systematically searched from inception to 1 December 2022. Observational studies reporting adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of risk factors for SSIs in patients undergoing PD were included. Two independent reviewers in teams performed data extraction, risk of bias assessment, and level of evidence analysis. The pooled results were estimated using a random-effects model. The I 2 statistic and Q χ 2 statistic were used to assess heterogeneity. Funnel plots, Egger's regression test, and the trim-and-fill method were used to determine publication bias. The primary outcomes were identifying risk factors for SSIs after PD. The secondary outcomes were the pooled prevalence rates of SSIs. Results: A total of 98 704 patients from 45 studies were included, and 80% of the studies were considered high quality. The estimated pooled prevalence of SSIs was 23% (0.19-0.27, I 2=97%). The prevalence of SSIs was found to be higher in Japan and lower in USA. Preoperative biliary stenting, higher body mass index (BMI), longer operation time, postoperative pancreatic fistula, soft pancreatic texture, perioperative blood transfusion, and cardiac disease were identified as significant risk factors for the development of SSIs after PD. Additionally, broad-spectrum antibiotics were a significant protective factor against SSIs. Subgroup analysis and sensitivity analysis showed that the results were robust. Conclusion and relevance: The prevalence of SSIs remains high and varies widely among regions. It is necessary to take effective preventive measures and carry out more prospective studies to further verify these results.

Predicting Anaerobic Membrane Bioreactor Performance Using Flow-Cytometry-Derived High and Low Nucleic Acid Content Cells.

Having a tool to monitor the microbial abundances rapidly and to utilize the data to predict the reactor performance would facilitate the operation of an anaerobic membrane bioreactor (AnMBR). This study aims to achieve the aforementioned scenario by developing a linear regression model that incorporates a time-lagging mode. The model uses low nucleic acid (LNA) cell numbers and the ratio of high nucleic acid (HNA) to LNA cells as an input data set. First, the model was trained using data sets obtained from a 35 L pilot-scale AnMBR. The model was able to predict the chemical oxygen demand (COD) removal efficiency and methane production 3.5 days in advance. Subsequent validation of the model using flow cytometry (FCM)-derived data (at time t - 3.5 days) obtained from another biologically independent reactor did not exhibit any substantial difference between predicted and actual measurements of reactor performance at time t. Further cell sorting, 16S rRNA gene sequencing, and correlation analysis partly attributed this accurate prediction to HNA genera (e.g., Anaerovibrio and unclassified Bacteroidales) and LNA genera (e.g., Achromobacter, Ochrobactrum, and unclassified Anaerolineae). In summary, our findings suggest that HNA and LNA cell routine enumeration, along with the trained model, can derive a fast approach to predict the AnMBR performance.

Machine learning algorithm to predict postoperative bleeding complications after lateral decubitus percutaneous nephrolithotomy.

Bleeding is a serious complication following percutaneous nephrolithotomy (PCNL). This study establishes a predictive model based on machine learning algorithms to forecast the occurrence of postoperative bleeding complications in patients with renal and upper ureteral stones undergoing lateral decubitus PCNL. We retrospectively collected data from 356 patients with renal stones and upper ureteral stones who underwent lateral decubitus PCNL in the Department of Urology at Peking University First Hospital-Miyun Hospital, between January 2015 and August 2022. Among them, 290 patients had complete baseline data. The data was randomly divided into a training group (n = 232) and a test group (n = 58) in an 8:2 ratio. Predictive models were constructed using Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGBoost). The performance of each model was evaluated using Accuracy, Precision, F1-Score, Receiver Operating Characteristic curves, and Area Under the Curve (AUC). Among the 290 patients, 35 (12.07%) experienced postoperative bleeding complications after lateral decubitus PCNL. Using postoperative bleeding as the outcome, the Logistic model achieved an accuracy of 73.2%, AUC of 0.605, and F1 score of 0.732. The Random Forest model achieved an accuracy of 74.5%, AUC of 0.679, and F1 score of 0.732. The XGBoost model achieved an accuracy of 68.3%, AUC of 0.513, and F1 score of 0.644. The predictive model for postoperative bleeding after lateral decubitus PCNL, established based on machine learning algorithms, is reasonably accurate. It can be utilized to predict postoperative stone residue and recurrence, aiding urologists in making appropriate treatment decisions.