Anomaly detection in multivariate time series data using deep ensemble models.

Anomaly detection in time series data is essential for fraud detection and intrusion monitoring applications. However, it poses challenges due to data complexity and high dimensionality. Industrial applications struggle to process high-dimensional, complex data streams in real time despite existing solutions. This study introduces deep ensemble models to improve traditional time series analysis and anomaly detection methods. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks effectively handle variable-length sequences and capture long-term relationships. Convolutional Neural Networks (CNNs) are also investigated, especially for univariate or multivariate time series forecasting. The Transformer, an architecture based on Artificial Neural Networks (ANN), has demonstrated promising results in various applications, including time series prediction and anomaly detection. Graph Neural Networks (GNNs) identify time series anomalies by capturing temporal connections and interdependencies between periods, leveraging the underlying graph structure of time series data. A novel feature selection approach is proposed to address challenges posed by high-dimensional data, improving anomaly detection by selecting different or more critical features from the data. This approach outperforms previous techniques in several aspects. Overall, this research introduces state-of-the-art algorithms for anomaly detection in time series data, offering advancements in real-time processing and decision-making across various industrial sectors.

Discovering the ecological structure of different macrophyte groups in rivers using non-parametric and parametric multivariate ordination techniques.

This paper analyses various methods of ecological ordering that are often used in modelling the relationship between vegetation and habitat. The results of direct gradient ordination by Canonical correspondence analysis (CCA), which is based on correlation, were compared with Non-metric multidimensional scaling (NMDS), which is based on rank analyses. Both tools were also compared with Detrended correspondence analysis (DCA), which is a popular indirect gradient analysis method. The macrophyte assessment was conducted at 98 river locations in the lowland regions of Poland. Each of the surveyed locations falls within a consistent abiotic category: small to medium-sized lowland rivers with a sandy bottom. Habitat elements analysed included limnological variables and geographic parameters, and the botanical survey focused on submerged macrophytes, including vascular plants, as well as bryophytes and algae. Firstly, it was shown that various analytical tools for determining the importance of ecological factors (Monte Carlo test, BIOENV) identify slightly different significant factors responsible for the development of macrophytes in rivers. Secondly, considerable similarity was found in the structure of macrophyte communities generated on NMDS and DCA biplots, while macrophyte communities were presented very differently based on CCA. Thirdly, the ecological preferences of aquatic plants based on one-dimensional analyses primarily reflected the results of CCA, whereas they did not always follow the ecological pattern revealed by NMDS. Finally, by conducting separate studies for non-vascular plants and vascular macrophytes, it was confirmed that different ecological drivers are responsible for the development of particular groups of macrophytes.

Prognostic analysis of rectal cancer patients after neoadjuvant chemoradiotherapy: different prognostic factors in patients with different TRGs.

PURPOSE: The extent of tumor regression varies widely among locally advanced rectal cancer (LARC) patients who receive neoadjuvant chemoradiotherapy (NCRT) followed by total mesorectal excision (TME). The purpose of this retrospectively study is to assess prognostic factors in LARC patients with NCRT, and further to analyze survival outcomes in patients with different tumor regression grades (TRGs). METHODS: This study includes LARC patients who underwent NCRT and TME at our institution. We retrospectively analyzed the clinicopathological characteristics and survival of all patients, and performed subgroup analysis for patients with different TRGs. Survival differences were compared using the Kaplan-Meier method and the log rank test. Additionally, a multiple Cox proportional hazard model was used to identify independent prognostic factors. RESULTS: The study included 393 patients, with 21.1%, 26.5%, 45.5%, and 6.9% achieving TRG 0, TRG 1, TRG 2, and TRG 3, respectively. The overall survival (OS) rate and disease-free survival (DFS) rate for all patients were 89.4% and 70.7%, respectively. Patients who achieved TRG 0-3 had different 5-year OS rates (96.9%, 91.1%, 85.2%, and 68.8%, P = 0.001) and 5-year DFS rates (80.8%, 72.4%, 67.0%, 55.8%, P = 0.031), respectively. Multivariate analyses showed that the neoadjuvant rectal (NAR) score was an independent prognostic indicator for both overall survival (OS) (HR = 4.040, 95% CI = 1.792-9.111, P = 0.001) and disease-free survival (DFS) (HR = 1.971, 95% CI = 1.478-2.628, P ˂ 0.001). In the subgroup analyses, the NAR score was found to be associated with DFS in patients with TRG 1 and TRG 2. After conducting multivariate analysis, it was found that ypT stage was a significant predictor of DFS for TRG 1 patients (HR = 4.384, 95% CI = 1.721-11.168, P = 0.002). On the other hand, ypN stage was identified as the dominant prognostic indicator of DFS for TRG 2 patients (HR = 2.795, 95% CI = 1.535-5.091, P = 0.001). However, none of these characteristics was found to be correlated with survival in patients with TRG 0 or TRG 3. CONCLUSION: NAR score, in particular, appears to be the most powerful prognostic factor. It is important to consider various prognostic predictors for patients with different TRGs.

[Multivariate analysis and construction and validation of a nomogram model from data of 1610 patients with non-tumor-related anastomotic stenosis after rectal cancer surgery].

Objective: To assess the risk factors affecting development of non-tumor- related anastomotic stenosis after rectal cancer and to construct a nomogram prediction model. Methods: This was a retrospective study of data of patients who had undergone excision with one-stage intestinal anastomosis for rectal cancer between January 2003 and September 2018 in Nanfang Hospital of Southern Medical University. The exclusion criteria were as follows: (1) pathological examination of the operative specimen revealed residual tumor on the incision margin of the anastomosis; (2) pathological examination of postoperative colonoscopy specimens revealed tumor recurrence at the anastomotic stenosis, or postoperative imaging evaluation and tumor marker monitoring indicated tumor recurrence; (3) follow-up time <3 months; and (4) simultaneous multiple primary cancers. Univariate analysis using the χ2 or Fisher's exact test was performed to assess the study patients' baseline characteristics and variables such as tumor-related factors and surgical approach (P<0.05). Multivariate analysis using binary logistic regression was then performed to identify independent risk factors for development of non-tumor-related anastomotic stenosis after rectal cancer. Finally, a nomogram model for predicting non-tumor-related anastomotic stenosis after rectal cancer surgery was constructed using R software. The reliability and accuracy of this prediction model was evaluated using internal validation and calculation of the area under the curve of the model's receiver characteristic curve (ROC). Results: The study cohort comprised 1,610 patients, including 1,008 men and 602 women of median age 59 (50, 67) years and median body mass index 22.4 (20.2, 24.5) kg/m². Non-tumor-related anastomotic stenosis developed in 121 (7.5%) of these patients. The incidence of non-tumor-related anastomotic stenosis in patients who had undergone neoadjuvant chemotherapy, neoadjuvant radiotherapy, and surgery alone was 11.2% (10/89), 26.4% (47/178), and 4.8% (64/1,343), respectively. Neoadjuvant treatment (neoadjuvant chemotherapy: OR=2.455, 95%CI: 1.148-5.253, P=0.021; neoadjuvant chemoradiotherapy, OR=3.882, 95%CI: 2.425-6.216, P<0.001), anastomotic leakage (OR=7.960, 95%CI: 4.550-13.926, P<0.001), open laparotomy (OR=3.412, 95%CI: 1.772-6.571, P<0.001), and tumor location (distance of tumor from the anal verge 5-10 cm: OR=2.381, 95%CI:1.227-4.691, P<0.001; distance of tumor from the anal verge <5 cm: OR=5.985,95% CI: 3.039-11.787, P<0.001) were identified as independent risk factors for non-tumor-related anastomotic stenosis. Thereafter, a nomogram prediction model incorporating the four identified risk factors for development of anastomotic stenosis after rectal cancer was developed. The area under the curve of the model ROC was 0.815 (0.773-0.857, P<0.001), and the C-index of the predictive model was 0.815, indicating that the model's calibration curve fitted well with the ideal curve. Conclusion: Non-tumor-related anastomotic stenosis after rectal cancer surgery is significantly associated with neoadjuvant treatment, anastomotic leakage, surgical procedure, and tumor location. A nomogram based on these four factors demonstrated good discrimination and calibration, and would therefore be useful for screening individuals at risk of anastomotic stenosis after rectal cancer surgery.

Development and validation of a simple and practical model for early detection of diabetic macular edema in patients with type 2 diabetes mellitus using easily accessible systemic variables.

OBJECTIVE: Diabetic macular edema (DME) is the leading cause of visual impairment in patients with diabetes mellitus (DM). The goal of early detection has not yet achieved due to a lack of fast and convenient methods. Therefore, we aim to develop and validate a prediction model to identify DME in patients with type 2 diabetes mellitus (T2DM) using easily accessible systemic variables, which can be applied to an ophthalmologist-independent scenario. METHODS: In this four-center, observational study, a total of 1994 T2DM patients who underwent routine diabetic retinopathy screening were enrolled, and their information on ophthalmic and systemic conditions was collected. Forward stepwise multivariable logistic regression was performed to identify risk factors of DME. Machine learning and MLR (multivariable logistic regression) were both used to establish prediction models. The prediction models were trained with 1300 patients and prospectively validated with 104 patients from Guangdong Provincial People's Hospital (GDPH). A total of 175 patients from Zhujiang Hospital (ZJH), 115 patients from the First Affiliated Hospital of Kunming Medical University (FAHKMU), and 100 patients from People's Hospital of JiangMen (PHJM) were used as external validation sets. Area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity, and specificity were used to evaluate the performance in DME prediction. RESULTS: The risk of DME was significantly associated with duration of DM, diastolic blood pressure, hematocrit, glycosylated hemoglobin, and urine albumin-to-creatinine ratio stage. The MLR model using these five risk factors was selected as the final prediction model due to its better performance than the machine learning models using all variables. The AUC, ACC, sensitivity, and specificity were 0.80, 0.69, 0.80, and 0.67 in the internal validation, and 0.82, 0.54, 1.00, and 0.48 in prospective validation, respectively. In external validation, the AUC, ACC, sensitivity and specificity were 0.84, 0.68, 0.90 and 0.60 in ZJH, 0.89, 0.77, 1.00 and 0.72 in FAHKMU, and 0.80, 0.67, 0.75, and 0.65 in PHJM, respectively. CONCLUSION: The MLR model is a simple, rapid, and reliable tool for early detection of DME in individuals with T2DM without the needs of specialized ophthalmologic examinations.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Multivariate Analysis of Solubility Parameters for Drug-Polymer Miscibility Assessment in Preparing Raloxifene Hydrochloride Amorphous Solid Dispersions.

The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

Neoadjuvant chemotherapy-induced hemoglobin decline as a prognostic factor in osteosarcoma around the knee joint: a single-center retrospective analysis of 242 patients.

PURPOSE: Anemia is relatively common in cancer patients, and is associated with poor survival in patients with various malignancies. However, how anemia would affect prognosis and response to neoadjuvant chemotherapy (NAC) in osteosarcoma (OS) is still without substantial evidence. METHODS: We retrospectively analysed 242 patients with stage II OS around the knee joint in our institute. Changed hemoglobin (Hb) levels (before and after NAC) were recorded to assess the prognostic value in DFS (disease-free survival) and tumor response to NAC. Univariate and multivariate analyses were conducted to identify prognostic factors related with outcome in OS patients. RESULTS: The mean Hb level significantly decreased after NAC (134.5 ± 15.3 g/L vs. 117.4 ± 16.3 g/L). The percentage of mild (21%), moderate (4.2%) and severe (0%) anemia patients markedly increased after NAC: 41%, 24% and 4.1% respectively. There was higher percentage of ≥ 5% Hb decline in patients with tumor necrosis rate < 90% (141 out of 161), compared with those with tumor necrosis rate ≥ 90% (59 out of 81). Further univariate and survival analysis demonstrated that Hb decline had a significant role in prediction survival in OS patients. Patients with ≥ 5% Hb decline after NAC had an inferior DFS compared with those with < 5% Hb decline. CONCLUSION: In osteosarcoma, patients with greater Hb decrease during neoadjuvant treatment were shown to have worse DFS and a poorer response to NAC than those without. Attempts to correct anemia and their effects on outcomes for osteosarcoma patients should be explored in future studies.

Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data.

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.

Prediction of lymph node metastasis in T1 colorectal cancer based on combination of body composition and vascular invasion.

OBJECTIVES: Lymph node metastasis (LNM) in colorectal cancer (CRC) patients is not only associated with the tumor's local pathological characteristics but also with systemic factors. This study aims to assess the feasibility of using body composition and pathological features to predict LNM in early stage colorectal cancer (eCRC) patients. METHODS: A total of 192 patients with T1 CRC who underwent CT scans and surgical resection were retrospectively included in the study. The cross-sectional areas of skeletal muscle, subcutaneous fat, and visceral fat at the L3 vertebral body level in CT scans were measured using Image J software. Logistic regression analysis were conducted to identify the risk factors for LNM. The predictive accuracy and discriminative ability of the indicators were evaluated using receiver operating characteristic (ROC) curves. Delong test was applied to compare area under different ROC curves. RESULTS: LNM was observed in 32 out of 192 (16.7%) patients with eCRC. Multivariate analysis revealed that the ratio of skeletal muscle area to visceral fat area (SMA/VFA) (OR = 0.021, p = 0.007) and pathological indicators of vascular invasion (OR = 4.074, p = 0.020) were independent risk factors for LNM in eCRC patients. The AUROC for SMA/VFA was determined to be 0.740 (p < 0.001), while for vascular invasion, it was 0.641 (p = 0.012). Integrating both factors into a proposed predictive model resulted in an AUROC of 0.789 (p < 0.001), indicating a substantial improvement in predictive performance compared to relying on a single pathological indicator. CONCLUSION: The combination of the SMA/VFA ratio and vascular invasion provides better prediction of LNM in eCRC.

Multivariate investigation of Moringa oleifera morpho-physiological and biochemical traits under various water regimes.

BACKGROUND: The climatic changes crossing the world menace the green life through limitation of water availability. The goal of this study was to determine whether Moringa oleifera Lam. trees cultivated under Tunisian arid climate, retain their tolerance ability to tolerate accentuated environmental stress factors such as drought and salinity. For this reason, the seeds of M. oleifera tree planted in Bouhedma Park (Tunisian arid area), were collected, germinated, and grown in the research area at the National Institute of Research in Rural Engineering, Waters and Forests (INRGREF) of Tunis (Tunisia). The three years aged trees were exposed to four water-holding capacities (25, 50, 75, and 100%) for 60 days to realise this work. RESULTS: Growth change was traduced by the reduction of several biometric parameters and fluorescence (Fv/Fm) under severe water restriction (25 and 50%). Whereas roots presented miraculous development in length face to the decrease of water availability (25 and 50%) in their rhizospheres. The sensitivity to drought-induced membrane damage (Malondialdehyde (MDA) content) and reactive oxygen species (ROS) liberation (hydrogen peroxide (H2O2) content) was highly correlated with ROS antiradical scavenging (ferric reducing antioxidant power (FRAP) and (2, 2'-diphenyl-1-picrylhydrazyle (DPPH)), phenolic components and osmolytes accumulation. The drought stress tolerance of M. oleifera trees was associated with a dramatic stimulation of superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), ascorbate peroxidase (APX), and glutathione peroxidase (GPX) activities. CONCLUSION: Based on the several strategies adopted, integrated M. oleifera can grow under drought stress as accentuated adverse environmental condition imposed by climate change.

Multivariate testing and effect size measures for batch effect evaluation in radiomic features.

While precision medicine applications of radiomics analysis are promising, differences in image acquisition can cause "batch effects" that reduce reproducibility and affect downstream predictive analyses. Harmonization methods such as ComBat have been developed to correct these effects, but evaluation methods for quantifying batch effects are inconsistent. In this study, we propose the use of the multivariate statistical test PERMANOVA and the Robust Effect Size Index (RESI) to better quantify and characterize batch effects in radiomics data. We evaluate these methods in both simulated and real radiomics features extracted from full-field digital mammography (FFDM) data. PERMANOVA demonstrated higher power than standard univariate statistical testing, and RESI was able to interpretably quantify the effect size of site at extremely large sample sizes. These methods show promise as more powerful and interpretable methods for the detection and quantification of batch effects in radiomics studies.

Multivariate genetic architecture reveals testosterone-driven sexual antagonism in contemporary humans.

Sex difference (SD) is ubiquitous in humans despite shared genetic architecture (SGA) between the sexes. A univariate approach, i.e., studying SD in single traits by estimating genetic correlation, does not provide a complete biological overview, because traits are not independent and are genetically correlated. The multivariate genetic architecture between the sexes can be summarized by estimating the additive genetic (co)variance across shared traits, which, apart from the cross-trait and cross-sex covariances, also includes the cross-sex-cross-trait covariances, e.g., between height in males and weight in females. Using such a multivariate approach, we investigated SD in the genetic architecture of 12 anthropometric, fat depositional, and sex-hormonal phenotypes. We uncovered sexual antagonism (SA) in the cross-sex-cross-trait covariances in humans, most prominently between testosterone and the anthropometric traits - a trend similar to phenotypic correlations. 27% of such cross-sex-cross-trait covariances were of opposite sign, contributing to asymmetry in the SGA. Intriguingly, using multivariate evolutionary simulations, we observed that the SGA acts as a genetic constraint to the evolution of SD in humans only when selection is sexually antagonistic and not concordant. Remarkably, we found that the lifetime reproductive success in both the sexes shows a positive genetic correlation with anthropometric traits, but not with testosterone. Moreover, we demonstrated that genetic variance is depleted along multivariate trait combinations in both the sexes but in different directions, suggesting absolute genetic constraint to evolution. Our results indicate that testosterone drives SA in contemporary humans and emphasize the necessity and significance of using a multivariate framework in studying SD.

Predictive role of ITPA genetic variants in thiopurine-related myelotoxicity in Crohn's disease patients.

Thiopurines, an effective therapy for Crohn's disease (CD), often lead to adverse events (AEs). Gene polymorphisms affecting thiopurine metabolism may predict AEs. This retrospective study in CD patients (n = 114) with TPMT activity > 5 Units/Red Blood Cells analyzed TPMT (c.238 G > C, c.460 G > A, c.719 A > G), ITPA (c.94 C > A, IVS2 + 21 A > C), and NUDT15 (c.415 C > T) polymorphisms. All patients received azathioprine (median dose 2.2 mg/kg) with 41.2% experiencing AEs, mainly myelotoxicity (28.1%). No NUDT15 polymorphisms were found, 7% had TPMT, and 31.6% had ITPA polymorphisms. AEs led to therapy modifications in 41.2% of patients. Multivariate analysis identified advanced age (OR 1.046, p = 0.007) and ITPA IVS2 + 21 A > C (OR 3.622, p = 0.015) as independent predictors of AEs. IVS2 + 21 A > C was also associated with myelotoxicity (OR 2.863, p = 0.021). These findings suggest that ITPA IVS2 + 21 A > C polymorphism and advanced age predict AEs during thiopurine therapy for CD with intermediate-normal TPMT activity.

dCCA: detecting differential covariation patterns between two types of high-throughput omics data.

MOTIVATION: The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes. RESULTS: We propose a novel approach called Differential Canonical Correlation Analysis (dCCA) to capture differential covariation patterns between two multivariate vectors across clinical groups. Unlike classical Canonical Correlation Analysis, which maximizes the correlation between two multivariate vectors, dCCA aims to maximally recover differentially expressed multivariate-to-multivariate covariation patterns between groups. We have developed computational algorithms and a toolkit to sparsely select paired subsets of variables from two sets of multivariate variables while maximizing the differential covariation. Extensive simulation analyses demonstrate the superior performance of dCCA in selecting variables of interest and recovering differential correlations. We applied dCCA to the Pan-Kidney cohort from the Cancer Genome Atlas Program database and identified differentially expressed covariations between noncoding RNAs and gene expressions. AVAILABILITY AND IMPLEMENTATION: The R package that implements dCCA is available at https://github.com/hwiyoungstat/dCCA.

Predictors of transition to schizophrenia and other long-lasting non-affective psychoses in first-episode patients with acute and transient psychotic disorders: A validation study

Background and objectives Almost half of the individuals with a first-episode of psychosis who initially meet criteria for acute and transient psychotic disorder (ATPD) will have had a diagnostic revision during their follow-up, mostly toward schizophrenia. This study aimed to determine the proportion of diagnostic transitions to schizophrenia and other long-lasting non-affective psychoses in patients with first-episode ATPD, and to examine the validity of the existing predictors for diagnostic shift in this population. Methods We designed a prospective two-year follow-up study for subjects with first-episode ATPD. A multivariate logistic regression analysis was performed to identify independent variables associated with diagnostic transition to persistent non-affective psychoses. This prediction model was built by selecting variables on the basis of clinical knowledge. Results Sixty-eight patients with a first-episode ATPD completed the study and a diagnostic revision was necessary in 30 subjects at the end of follow-up, of whom 46.7% transited to long-lasting non-affective psychotic disorders. Poor premorbid adjustment and the presence of schizophreniform symptoms at onset of psychosis were the only variables independently significantly associated with diagnostic transition to persistent non-affective psychoses. Conclusion Our findings would enable early identification of those inidividuals with ATPD at most risk for developing long-lasting non-affective psychotic disorders, and who therefore should be targeted for intensive preventive interventions. (AU)

Who is at risk of lung nodules on low-dose CT in a Western country? A population-based approach.

BACKGROUND: This population-based study aimed to identify the risk factors for lung nodules in a Western European general population. METHODS: We quantified the presence or absence of lung nodules among 12 055 participants of the Dutch population-based ImaLife (Imaging in Lifelines) study (age ≥45 years) who underwent low-dose chest computed tomography. Outcomes included the presence of 1) at least one solid lung nodule (volume ≥30 mm3) and 2) a clinically relevant lung nodule (volume ≥100 mm3). Fully adjusted multivariable logistic regression models were applied overall and stratified by smoking status to identify independent risk factors for the presence of nodules. RESULTS: Among the 12 055 participants (44.1% male; median age 60 years; 39.9% never-smokers; 98.7% White), we found lung nodules in 41.8% (5045 out of 12 055) and clinically relevant nodules in 11.4% (1377 out of 12 055); the corresponding figures among never-smokers were 38.8% and 9.5%, respectively. Factors independently associated with increased odds of having any lung nodule included male sex, older age, low educational level, former smoking, asbestos exposure and COPD. Among never-smokers, a family history of lung cancer increased the odds of both lung nodules and clinically relevant nodules. Among former and current smokers, low educational level was positively associated with lung nodules, whereas being overweight was negatively associated. Among current smokers, asbestos exposure and low physical activity were associated with clinically relevant nodules. CONCLUSIONS: The study provides a large-scale evaluation of lung nodules and associated risk factors in a Western European general population: lung nodules and clinically relevant nodules were prevalent, and never-smokers with a family history of lung cancer were a non-negligible group.

Recurrence of rectal cancer on the pelvic sidewall after lateral lymph node dissection.

PURPOSE: Although lateral lymph node dissection has been performed to prevent lateral pelvic recurrence in locally advanced lower rectal cancer, the incidence of lateral pelvic recurrence after this procedure has not been investigated. Therefore, this study aimed to investigate the long-term outcomes of patients who underwent lateral pelvic lymph node dissection, with a particular focus on recurrence patterns. METHODS: This was a retrospective study conducted at a single high-volume cancer center in Japan. A total of 493 consecutive patients with stage II-III rectal cancer who underwent lateral lymph node dissection between January 2005 and August 2022 were included. The primary outcome measures included patterns of recurrence, overall survival, and relapse-free survival. Patterns of recurrence were categorized as lateral or central pelvic. RESULTS: Among patients who underwent lateral lymph node dissection, 18.1% had pathologically positive lateral lymph node metastasis. Lateral pelvic recurrence occurred in 5.5% of patients after surgery. Multivariate analysis identified age > 75 years, lateral lymph node metastasis, and adjuvant chemotherapy as independent risk factors for lateral pelvic recurrence. Evaluation of the recurrence rate by dissection area revealed approximately 1% of recurrences in each area after dissection. CONCLUSION: We demonstrated the prognostic outcome and limitations of lateral lymph node dissection for patients with advanced lower rectal cancer, focusing on the incidence of recurrence in the lateral area after the dissection. Our study emphasizes the clinical importance of lateral lymph node dissection, which is an essential technique that surgeons should acquire.

In-depth analysis of volatolomic and odorous profiles of novel craft beer by permutation test features selection and multivariate correlation analysis.

This research explored the impact of binary cereal blends [barley with durum wheat (DW) and soft wheat (CW)], four autochthonous yeast strains (9502, 9518, 14061 and 17290) and two refermentation sugar concentrations (6-9 g/L), on volatolomics (VOCs) and odour profiles of craft beers using unsupervised statistics. For the first time, we applied permutation test to select volatiles with higher significance in explaining variance among samples. The unsupervised approach on the 19 selected VOCs revealed cereal-yeast interaction to be the main source of variability and DW-9502-6/9, DW-17290-6, CW-17290-6 and CW-9518-6 being the best technological strategies. In particular, in samples DW-9502-6/9, concentrations of some of the selected volatiles were observed to be approximately three to more than seven times higher than the average. PLS-correlation between VOCs and odour profiles proved to be very useful in assessing the weight of each of the selected VOCs on the perception of odour notes.