Weighted Gene Co-expression Network Analysis (WGCNA) is a widely used approach for the generation of gene co-expression networks. However, networks generated with this tool usually create large modules with a large set of functional annotations hard to decipher. We have developed TGCN, a new method to create Targeted Gene Co-expression Networks. This method identifies the transcripts that best predict the trait of interest based on gene expression using a refinement of the LASSO regression. Then, it builds the co-expression modules around those transcripts. Algorithm properties were characterized using the expression of 13 brain regions from the Genotype-Tissue Expression project. When comparing our method with WGCNA, TGCN networks lead to more precise modules that have more specific and yet rich biological meaning. Then, we illustrate its applicability by creating an APP-TGCN on The Religious Orders Study and Memory and Aging Project dataset, aiming to identify the molecular pathways specifically associated with APP role in Alzheimer\'s disease. Main biological findings were further validated in two independent cohorts. In conclusion, we provide a new framework that serves to create targeted networks that are smaller, biologically relevant and useful in high throughput hypothesis driven research. The TGCN R package is available on Github: https://github.com/aliciagp/TGCN .

UNASSIGNED: Acute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis of AIS. We aimed to identify the most promising prognostic indicators and establish prognostic models for AIS.
UNASSIGNED: 484 subjects enrolled from four centers were analyzed immunophenotypic indicators of peripheral blood by flow cytometry. Least absolute shrinkage and selection operator (LASSO) regression was applied to minimize the potential collinearity and over-fitting of variables measured from the same subject and over-fitting of variables. Univariate and multivariable Cox survival analysis of differences between and within cohorts was performed by log-rank test. The areas under the receiving operating characteristic (ROC) curves were used to evaluate the selection accuracy of immunophenotypic indicators in identifying AIS subjects with survival risk. The prognostic model was constructed using a multivariate Cox model, consisting of 402 subjects as a training cohort and 82 subjects as a testing cohort.
UNASSIGNED: In the prospective study, 7 immunophenotypic indicators of distinct significance were screened out of 72 peripheral blood immunophenotypic indicators by LASSO. In multivariate cox regression, CTL (%) [HR: 1.18, 95% CI: 1.03-1.33], monocytes/μl [HR: 1.13, 95% CI: 1.05-1.21], non-classical monocytes/μl [HR: 1.09, 95% CI: 1.02-1.16] and CD56high NK cells/μl [HR: 1.13, 95% CI: 1.05-1.21] were detected to decrease the survival probability of AIS, while Tregs/μl [HR:0.97, 95% CI: 0.95-0.99, p=0.004], BM/μl [HR:0.90, 95% CI: 0.85-0.95, p=0.023] and CD16+NK cells/μl [HR:0.93, 95% CI: 0.88-0.98, p=0.034] may have the protective effect. As for indicators\' discriminative ability, the AUC for CD56highNK cells/μl attained the highest of 0.912. In stratification analysis, the survival probability for AIS subjects with a higher level of Tregs/μl, BM/μl, CD16+NK cells/μl, or lower levels of CD56highNK cells/μl, CTL (%), non-classical monocytes/μl, Monocytes/μl were more likely to survive after AIS. The multivariate Cox model showed an area under the curve (AUC) of 0.805, 0.781 and 0.819 and 0.961, 0.924 and 0.982 in the training and testing cohort, respectively.
UNASSIGNED: Our study identified 7 immunophenotypic indicators in peripheral blood may have great clinical significance in monitoring the prognosis of AIS and provide a convenient and valuable predictive model for AIS.

BACKGROUND: The molecular pathways linking short and long sleep duration with incident diabetes mellitus (iDM) and incident coronary heart disease (iCHD) are not known. We aimed to identify circulating protein patterns associated with sleep duration and test their impact on incident cardiometabolic disease.
METHODS: We assessed sleep duration and measured 78 plasma proteins among 3336 participants aged 46-68 years, free from DM and CHD at baseline, and identified cases of iDM and iCHD using national registers. Incident events occurring in the first 3 years of follow-up were excluded from analyses. Tenfold cross-fit partialing-out lasso logistic regression adjusted for age and sex was used to identify proteins that significantly predicted sleep duration quintiles when compared with the referent quintile 3 (Q3). Predictive proteins were weighted and combined into proteomic scores (PS) for sleep duration Q1, Q2, Q4, and Q5. Combinations of PS were included in a linear regression model to identify the best predictors of habitual sleep duration. Cox proportional hazards regression models with sleep duration quintiles and sleep-predictive PS as the main exposures were related to iDM and iCHD after adjustment for known covariates.
RESULTS: Sixteen unique proteomic markers, predominantly reflecting inflammation and apoptosis, predicted sleep duration quintiles. The combination of PSQ1 and PSQ5 best predicted sleep duration. Mean follow-up times for iDM (n = 522) and iCHD (n = 411) were 21.8 and 22.4 years, respectively. Compared with sleep duration Q3, all sleep duration quintiles were positively and significantly associated with iDM. Only sleep duration Q1 was positively and significantly associated with iCHD. Inclusion of PSQ1 and PSQ5 abrogated the association between sleep duration Q1 and iDM. Moreover, PSQ1 was significantly associated with iDM (HR = 1.27, 95% CI: 1.06-1.53). PSQ1 and PSQ5 were not associated with iCHD and did not markedly attenuate the association between sleep duration Q1 with iCHD.
CONCLUSIONS: We here identify plasma proteomic fingerprints of sleep duration and suggest that PSQ1 could explain the association between very short sleep duration and incident DM.

BACKGROUND: An atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making.
METHODS: We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children\'s Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.
RESULTS: The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS.
CONCLUSIONS: In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

BACKGROUND: Condom use at last intercourse is an effective indicator for human immunodeficiency virus (HIV) prevention. To identify at-risk individuals and improve prevention strategies, this study explored factors associated with condomless sex at last intercourse in the last year and developed a risk estimation model to calculate the individual possibility of condomless sex among college students in Zhuhai, China.
METHODS: A cross-sectional study was conducted among 1430 college students who had sex in the last year from six universities in Zhuhai. The least absolute shrinkage and selection operator (LASSO) and logistic regression were performed to explore the predictors of condomless sex. The nomogram was constructed to calculate the individual possibility of condomless sex. Discrimination and calibration of the nomogram were evaluated using the area under the receiver-operator characteristic curve (AUROC) and the calibration curve.
RESULTS: The proportion of students who had condomless sex at last intercourse was 18.2% (260/1430). Students who had experienced more types of intimate partner violence (aOR, 1.58; 95% CI, 1.31 ~ 1.92) and had anal sex (aOR, 1.75; 95% CI, 1.06 ~ 2.84) were more likely to have condomless sex. Students who had heterosexual intercourse (aOR, 0.37; 95% CI, 0.21 ~ 0.70), used condoms at first sex (aOR, 0.20; 95% CI, 0.14 ~ 0.27), had high attitudes towards condom use (aOR, 0.87; 95% CI, 0.80 ~ 0.95) and self-efficacy for condom use (aOR, 0.84; 95% CI, 0.78 ~ 0.90) were less likely to have condomless sex. The nomogram had high accuracy with an AUROC of 0.83 and good discrimination.
CONCLUSIONS: Intimate partner violence, anal sex, condom use at first sex, attitude towards condom use, and self-efficacy for condom use were associated with condomless sex among college students. The nomogram was an effective and convenient tool for calculating the individualized possibility of condomless sex among college students. It could help to identify individuals at risk and help universities and colleges to formulate appropriate individualized interventions and sexual health education programs.

UNASSIGNED: To construct and validate a precise and personalized predictive model for non-alcoholic fatty liver disease (NAFLD) to enhance NAFLD screening and healthcare administration.
UNASSIGNED: A total of 730 participants\' clinical information and outcome measurements were gathered and randomly divided into training and validation sets in a ratio of 3:7. Using the least absolute shrinkage and selection operator (LASSO) regression and multiple logistic regression, a nomogram was established to select risk predictor variables. The NAFLD prediction model was validated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA).
UNASSIGNED: After random grouping, the cohort comprised 517 in the training set and 213 in the validation set. The prediction model employed nine of the 20 selected variables, namely gender, hypertension, waist circumference, body mass index, blood platelet, triglycerides, high-density lipoprotein cholesterol, plasma glucose, and alanine aminotransferase. ROC curve analysis yielded an area under the curve values of 0.877 (95% Confidence Interval [CI]: 0.848-0.907) for the training set and 0.871 (95% CI: 0.825-0.917) for the validation set. Optimal critical values were determined as 0.472 (0.786, 0.825) in the training set and 0.457 (0.743, 0.839) in the validation set. Calibration curves for both sets showed proximity to the ideal diagonal, with P-values of 0.972 and 0.370 for the training and validation sets, respectively (P > 0.05). DCA indicated favorable clinical applicability of the model.
UNASSIGNED: We constructed a nomogram model that could complement traditional NAFLD detection methods, aiding in individualized risk assessment for NAFLD.

UNASSIGNED: To develop a dynamic nomogram of subsyndromal delirium (SSD) in intensive care unit (ICU) patients and internally validate its efficacy in predicting SSD.
UNASSIGNED: Patients who met the inclusion and exclusion criteria in the ICU of a tertiary hospital in Zhejiang from September 2021 to June 2022 were selected as the research objects. The patient data were randomly divided into the training set and validation set according to the ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were used to screen the predictors of SSD, and R software was used to construct a dynamic nomogram. Receiver operating characteristic (ROC) curve, calibration band and decision curve were used to evaluate the discrimination, calibration and clinical effectiveness of the model.
UNASSIGNED: A total of 1000 eligible patients were included, including 700 in the training set and 300 in the validation set. Age, drinking history, C reactive protein level, APACHE II, indwelling urinary catheter, mechanical ventilation, cerebrovascular disease, respiratory failure, constraint, dexmedetomidine, and propofol were predictors of SSD in ICU patients. The ROC curve values of the training set was 0.902 (95% confidence interval: 0.879-0.925), the best cutoff value was 0.264, the specificity was 78.4%, and the sensitivity was 88.0%. The ROC curve values of the validation set was 0.888 (95% confidence interval: 0.850-0.930), the best cutoff value was 0.543, the specificity was 94.9%, and the sensitivity was 70.9%. The calibration band showed good calibration in the training and validation set. Decision curve analysis showed that the net benefit in the model was significantly high.
UNASSIGNED: The dynamic nomogram has good predictive performance, so it is a precise and effective tool for medical staff to predict and manage SSD in the early stage.

Prognostic factors for postoperative early recurrence (ER) of gastric cancer (GC) in patients with normal or abnormal preoperative tumor markers (pre-TMs) remain unclear.
2875 consecutive patients with GC who underwent radical gastrectomy (RG) between January 2010 and December 2016 were enrolled and randomly divided into training and internal validation groups. ER was defined as recurrence within two years of gastrectomy. Normal pre-TMs were defined as CEA≤5 ng/mL and CA199 ≤ 37 U/mL. Least absolute shrinkage selection operator (LASSO) Cox regression analysis was used to screen ER predictors. The scoring model was validated using 546 patients from another hospital.
A total of 3421 patients were included. Multivariate Cox analysis showed that pre-TMs was an independent prognostic factor for ER. Survival after ER was equally poor in the normal and abnormal pre-TMs groups (P = 0.160). Based on LASSO Cox regression, the ER of patients with abnormal pre-TMs was only associated with the pT and pN stages; however, in patients with normal pre-TMs, it was also associated with tumor size, perineural invasion, and prognostic nutritional index. Scoring model constructed for patients with normal pre-TMs had better predictive performance than TNM staging (concordance-index:0.826 vs. 0.807, P < 0.001) and good reproducibility in both validation sets. Moreover, through risk stratification, the scoring model could not only identify the risk of ER but also distinguish ER patterns and adjuvant chemotherapy benefit subgroups.
pre-TMs is an independent prognostic factor for ER in GC after RG. The established scoring model demonstrates excellent predictive performance and clinical utility.

UNASSIGNED: Tuberculosis (TB) is a major public health concern, particularly among people living with the Human immunodeficiency Virus (PLWH). Accurate prediction of TB disease in this population is crucial for early diagnosis and effective treatment. Logistic regression and regularized machine learning methods have been used to predict TB, but their comparative performance in HIV patients remains unclear. The study aims to compare the predictive performance of logistic regression with that of regularized machine learning methods for TB disease in HIV patients.
UNASSIGNED: Retrospective analysis of data from HIV patients diagnosed with TB in three hospitals in Kisumu County (JOOTRH, Kisumu sub-county hospital, Lumumba health center) between [dates]. Logistic regression, Lasso, Ridge, Elastic net regression were used to develop predictive models for TB disease. Model performance was evaluated using accuracy, and area under the receiver operating characteristic curve (AUC-ROC).
UNASSIGNED: Of the 927 PLWH included in the study, 107 (12.6%) were diagnosed with TB. Being in WHO disease stage III/IV (aOR: 7.13; 95%CI: 3.86-13.33) and having a cough in the last 4 weeks (aOR: 2.34;95%CI: 1.43-3.89) were significant associated with the TB. Logistic regression achieved accuracy of 0.868, and AUC-ROC of 0.744. Elastic net regression also showed good predictive performance with accuracy, and AUC-ROC values of 0.874 and 0.762, respectively.
UNASSIGNED: Our results suggest that logistic regression, Lasso, Ridge regression, and Elastic net can all be effective methods for predicting TB disease in HIV patients. These findings may have important implications for the development of accurate and reliable models for TB prediction in HIV patients.

UNASSIGNED: This study aims to establish hypertension risk nomograms for Chinese male and female adults, respectively.
UNASSIGNED: A series of questionnaire surveys, physical assessments, and biochemical indicator tests were performed on 18,367 adult participants in China. The optimization of variable selection was conducted by running cyclic coordinate descent with 10-fold cross-validation through the least absolute shrinkage and selection operator (LASSO) regression. The nomograms were built by including the predictors selected through multivariable logistic regression. Calibration plots, receiver operating characteristic curves (ROC), decision curve analysis (DCA), clinical impact curves (CIC), and net reduction curve plots (NRC) were used to validate the models.
UNASSIGNED: Out of a total of 18 variables, 5 predictors-namely age, body mass index, waistline, hipline, and resting heart rate-were identified for the hypertension risk predictive model for men with an area under the ROC of 0.693 in the training set and 0.707 in the validation set. Seven predictors-namely age, body mass index, body weight, cardiovascular disease history, waistline, resting heart rate, and daily activity level-were identified for the hypertension risk predictive model for women with an area under the ROC of 0.720 in the training set and 0.748 in the validation set. The nomograms for both men and women were externally well-validated.
UNASSIGNED: Gender differences may induce heterogeneity in hypertension risk prediction between men and women. Besides basic demographic and anthropometric parameters, information related to the functional status of the cardiovascular system and physical activity appears to be necessary.