Predictive modeling of perioperative patient deterioration: combining unanticipated ICU admissions and mortality for improved risk prediction.

OBJECTIVE: This paper presents a comprehensive analysis of perioperative patient deterioration by developing predictive models that evaluate unanticipated ICU admissions and in-hospital mortality both as distinct and combined outcomes. MATERIALS AND METHODS: With less than 1% of cases resulting in at least one of these outcomes, we investigated 98 features to identify their role in predicting patient deterioration, using univariate analyses. Additionally, multivariate analyses were performed by employing logistic regression (LR) with LASSO regularization. We also assessed classification models, including non-linear classifiers like Support Vector Machines, Random Forest, and XGBoost. RESULTS: During evaluation, careful attention was paid to the data imbalance therefore multiple evaluation metrics were used, which are less sensitive to imbalance. These metrics included the area under the receiver operating characteristics, precision-recall and kappa curves, and the precision, sensitivity, kappa, and F1-score. Combining unanticipated ICU admissions and mortality into a single outcome improved predictive performance overall. However, this led to reduced accuracy in predicting individual forms of deterioration, with LR showing the best performance for the combined prediction. DISCUSSION: The study underscores the significance of specific perioperative features in predicting patient deterioration, especially revealed by univariate analysis. Importantly, interpretable models like logistic regression outperformed complex classifiers, suggesting their practicality. Especially, when combined in an ensemble model for predicting multiple forms of deterioration. These findings were mostly limited by the large imbalance in data as post-operative deterioration is a rare occurrence. Future research should therefore focus on capturing more deterioration events and possibly extending validation to multi-center studies. CONCLUSIONS: This work demonstrates the potential for accurate prediction of perioperative patient deterioration, highlighting the importance of several perioperative features and the practicality of interpretable models like logistic regression, and ensemble models for the prediction of several outcome types. In future clinical practice these data-driven prediction models might form the basis for post-operative risk stratification by providing an evidence-based assessment of risk.

Multivariate analysis of the summer herbaceous vegetation and environmental factors of the sub-tropical region.

Understanding the distribution of the plant species of an unexplored area is the utmost need of the present-day. In order to collect vegetation data, Quadrat method was used having size of 1 m2. The composite soil samples from each site were tested for various edaphic properties. PC-ORD v.5 was used for the classification of the vegetation while CANOCO v.5.1 was used for ordination of the data and to find out the complex relationship between plants and environment. Survey was conducted during summer season and a total of 216 herbaceous species were recorded from forty different sites of District Kohat, Pakistan. Cluster Analysis (CA) and Two-Way Cluster Analysis (TWCA) classified the vegetation of forty sites into six major plant groups i.e., 1. Paspalum paspalodes, Alternanthera sessilis, Typha domingensis, 2. Cynodon dactylon, Parthenium hysterophorus, Brachiaria ramosa, 3. Cynodon dactylon, Eragrostis minor, Cymbopogon jwarancusa, 4. Cymbopogon jwarancusa, Aristida adscensionis, Boerhavia procumbens, 5. Cymbopogon jwarancusa, Aristida adscensionis, Pennisetum orientale and 6. Heteropogon contortus, Bothriochloa ischaemum, Chrysopogon serrulatus. They were named after the dominant species based on their Importance Value (IV). The detrended correspondence analysis (DCA) analysis further confirmed the vegetation classification. Canonical correspondence analysis (CCA) indicated that the species distribution in the area was strongly affected by various environmental factors including status, soil characteristics, topography and altitude.

Estimation for Raw Material Plants of a Henna Product Using LC-High Resolution MS and Multivariate Analysis.

Henna is a plant-based dye obtained from the powdered leaf of the pigmented plant Lawsonia inermis, and has often been used for grey hair dyeing, treatment, and body painting. As a henna product, the leaves of Indigofera tinctoria and Cassia auriculata can be blended to produce different colour variations. Although allergy from henna products attributed to p-phenylenediamine, which is added to enhance the dye, is reported occasionally, raw material plants of henna products could also contribute to the allergy. In this study, we reported that raw material plants of commercial henna products distributed in Japan can be estimated by LC-high resolution MS (LC-HRMS) and multivariate analysis. Principal Component Analysis (PCA) score plot clearly separated 17 samples into three groups [I; henna, II; blended henna primarily comprising Indigofera tinctoria, III; Cassia auriculata]. This grouping was consistent with the ingredient lists of products except that one sample listed as henna was classified as Group III, indicating that its ingredient label may differ from the actual formulation. The ingredients characteristic to Groups I, II, and III by PCA were lawsone (1), indirubin (2), and rutin (3), respectively, which were reported to be contained in each plant as ingredients. Therefore, henna products can be considered to have been manufactured from these plants. This study is the first to estimate raw material plants used in commercial plant-based dye by LC-HRMS and multivariate analysis.

Investigation of different cold adaptation abilities in Salmonella enterica serotype Typhimurium strains using extracellular metabolomic approach.

This study explored the extracellular metabolomic responses of three different Salmonella enterica serotype Typhimurium (S. Typhimurium) strains-ATCC 13311 (STy1), NCCP 16964 (STy4), and NCCP 16958 (STy8)-cultured at refrigeration temperatures. The objective was to identify the survival mechanisms of S. Typhimurium under cold stress by analyzing variations in their metabolomic profiles. Qualitative and quantitative assessments identified significant metabolite alterations on day 6, marking a critical inflection point. Key metabolites such as trehalose, proline, glycerol, and tryptophan were notably upregulated in response to cold stress. Through multivariate analyses, the strains were distinguished using three metabolites-4-aminobutyrate, ethanol, and uridine-as potential biomarkers, underscoring distinct metabolic responses to refrigeration. Specifically, STy1 exhibited unique adaptive capabilities through enhanced metabolism of betaine and 4-aminobutyrate. These findings highlight the variability in adaptive strategies among S. Typhimurium strains, suggesting that certain strains may possess more robust metabolic pathways for enhancing survival in refrigerated conditions.

Serum macroelements and microelements levels in periparturient dairy cows in relation to fatty liver diseases.

BACKGROUND: Fatty liver in dairy cows is a common metabolic disease defined by triglyceride (TG) buildup in the hepatocyte. Clinical diagnosis of fatty liver is usually done by liver biopsy, causing considerable economic losses in the dairy industry owing to the lack of more effective diagnostic methods. Therefore, this study aimed to investigate the potential utility of blood biomarkers for the diagnosis and early warning of fatty liver in dairy cows. RESULTS: A total of twenty-four lactating cows within 28 days after parturition were randomly selected as experimental animals and divided into healthy cows (liver biopsy tested, n = 12) and cows with fatty liver (liver biopsy tested, n = 12). Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine the macroelements and microelements in the serum of two groups of cows. Compared to healthy cows (C), concentrations of calcium (Ca), potassium (K), magnesium (Mg), strontium (Sr), selenium (Se), manganese (Mn), boron (B) and molybdenum (Mo) were lower and copper (Cu) was higher in fatty liver cows (F). Meanwhile, the observed differences in macroelements and microelements were related to delivery time, with the greatest major disparity between C and F occurring 7 days after delivery. Multivariable analysis was used to test the correlation between nine serum macroelements, microelements and fatty liver. Based on variable importance projection and receiver operating characteristic (ROC) curve analysis, minerals Ca, Se, K, B and Mo were screened as the best diagnostic indicators of fatty liver in postpartum cows. CONCLUSIONS: Our data suggested that serum levels of Ca, K, Mg, Se, B, Mo, Mn, and Sr were lower in F than in C. The most suitable period for an early-warning identification of fatty liver in cows was 7 days after delivery, and Ca, Se, K, B and Mo were the best diagnostic indicators of fatty liver in postpartum cows.

Multidimensional impact of sport types on the psychological well-being of student athletes: A multivariate investigation.

The correlation between sports participation and psychological well-being is well-documented, revealing a complex interplay influenced by competition level and cultural context. This is particularly relevant in Korea, where the university sports culture significantly impacts student life. This study evaluates how competitive versus non-competitive sports affect Korean university students' psychological well-being using a quantitative approach with SmartPLS 4 for multi-group analysis. Findings reveal that competitive sports significantly enhance mental toughness and stress management through structured coping mechanisms and robust social support, improving coping strategy effectiveness by 34 % compared to non-competitive sports. Conversely, participants in non-competitive sports experience greater general well-being with a 40 % higher use of informal support. These insights suggest that university sports programs could benefit from targeted interventions incorporating specific coping strategies and social support frameworks tailored to the competitive context. This research underscores the need for precise stress management techniques and resilience-building exercises in sports curricula to optimize psychological well-being across different sports environments in Korean universities.

Exploring seasonal variability in water quality of Nyabarongo River in Rwanda via water quality indices and DPSIR modelling.

Understanding seasonal variations in water quality is crucial for effective management of freshwater rivers amidst changing environmental conditions. This study employed water quality index (WQI), metal index (MI), and pollution indices (PI) to comprehensively assess water quality and pollution levels in Nyabarongo River of Rwanda. A dynamic driver-pressure-state-impact-response model was used to identify factors influencing quality management. Over 4 years (2018-2021), 69 samples were collected on a monthly basis from each of the six monitoring stations across the Nyabarongo River throughout the four different seasons. Maximum WQI values were observed during dry long (52.90), dry short (21.478), long rain (93.66), and short rain (37.4) seasons, classified according to CCME 2001 guidelines. Ion concentrations exceeded WHO standards, with dominant ions being HCO 3 - and Mg 2 + . Variations in water quality were influenced by factors such as calcium bicarbonate dominance in dry seasons and sodium sulfate dominance in rainy seasons. Evaporation and precipitation processes primarily influenced ionic composition. Metal indices (MI) exhibited wide ranges: long dry (0.2-433.0), short dry (0.1-174.3), long rain (0.1-223.7), and short rain (0.3-252.5). The hazard index values for Cu2+, Mn4+, Zn2+, and Cr3+ were below 1, ranging from 8.89E - 08 to 7.68E - 07 for adults and 2.30E - 07 to 5.02E - 06 for children through oral ingestion, and from 6.68E - 10 to 5.07E - 07 for adults and 6.61E - 09 to 2.54E - 06 for children through dermal contact. With a total carcinogenic risk of less than 1 for both ingestion and dermal contact, indicating no significant health risks yet send strong signals to Governmental management of the Nyabarongo River. Overall water quality was classified as marginal in long dry, poor in short dry, good in long rain, and poor again in short rain seasons.

CT evaluation of bowel wall enhancement in pneumatosis intestinalis: preventing non-therapeutic laparotomies.

PURPOSE: To evaluate the diagnostic performance of bowel wall enhancement for diagnosing concomitant bowel ischemia in patients with parietal pneumatosis (PI) diagnosed at abdominal CT. MATERIALS AND METHODS: From January 1, 2012 to December 31, 2021, 226 consecutive patients who presented with PI on abdominal CT from any bowel segment were included. Variables at the time of the CT were retrospectively extracted from medical charts. CT examinations were blindly analyzed by two independent radiologists. The third reader classified all disagreement of bowel enhancement in three categories: (1) normal bowel enhancement; (2) doubtful bowel wall enhancement; (3) absent bowel wall enhancement. Multivariable logistic regression analysis was performed. Concomitant bowel ischemia was defined as requirement of bowel resection specifically due to ischemic lesion in operated patients and death from bowel ischemia in non-operated patients. RESULTS: Overall, 78/226 (35%) patients had PI associated with concomitant bowel ischemia. At multivariate analysis, Only absence or doubtful bowel wall enhancement was associated with concomitant bowel ischemia (OR = 167.73 95%CI [23.39-4349.81], P < 0,001) and acute mesenteric ischemia associated with PP (OR = 67.94; 95%CI [5.18-3262.36], P < 0.009). Among the 82 patients who underwent a laparotomy for suspected bowel ischemia, rate of non-therapeutic laparotomy increased from 15/59 (25%), 2/6 (50%) and 16/17 (94%) when bowel wall enhancement was absent, doubtful and normal respectively. CONCLUSION: Absence of enhancement of the bowel wall is the primary feature associated with concomitant bowel ischemia. It should be carefully assessed when PI is detected to avoid non-therapeutic laparotomy.

Predicting progression from subjective cognitive decline to mild cognitive impairment or dementia based on brain atrophy patterns.

BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder where pathophysiological changes begin decades before the onset of clinical symptoms. Analysis of brain atrophy patterns using structural MRI and multivariate data analysis are an effective tool in identifying patients with subjective cognitive decline (SCD) at higher risk of progression to AD dementia. Atrophy patterns obtained from models trained to classify advanced AD versus normal subjects, may not be optimal for subjects at an early stage, like SCD. In this study, we compared the accuracy of the SCD progression prediction using the 'severity index' generated using a standard classification model trained on patients with AD dementia versus a new model trained on ß-amyloid (Aß) positive patients with amnestic mild cognitive impairment (aMCI). METHODS: We used structural MRI data of 504 patients from the Swedish BioFINDER-1 study cohort (cognitively normal (CN), Aß-negative = 220; SCD, Aß positive and negative = 139; aMCI, Aß-positive = 106; AD dementia = 39). We applied multivariate data analysis to create two predictive models trained to discriminate CN individuals from either individuals with Aß positive aMCI or AD dementia. Models were applied to individuals with SCD to classify their atrophy patterns as either high-risk "disease-like" or low-risk "CN-like". Clinical trajectory and model accuracy were evaluated using 8 years of longitudinal data. RESULTS: In predicting progression from SCD to MCI or dementia, the standard, dementia-based model, reached 100% specificity but only 10.6% sensitivity, while the new, aMCI-based model, reached 72.3% sensitivity and 60.9% specificity. The aMCI-based model was superior in predicting progression from SCD to MCI or dementia, reaching a higher receiver operating characteristic area under curve (AUC = 0.72; P = 0.037) in comparison with the dementia-based model (AUC = 0.57). CONCLUSION: When predicting conversion from SCD to MCI or dementia using structural MRI data, prediction models based on individuals with milder levels of atrophy (i.e. aMCI) may offer superior clinical value compared to standard dementia-based models.

Linear and Non-Linear Modelling Methods for a Gas Sensor Array Developed for Process Control Applications.

New process developments linked to Power to X (energy storage or energy conversion to another form of energy) require tools to perform process monitoring. The main gases involved in these types of processes are H2, CO, CH4, and CO2. Because of the non-selectivity of the sensors, a multi-sensor matrix has been built in this work based on commercial sensors having very different transduction principles, and, therefore, providing richer information. To treat the data provided by the sensor array and extract gas mixture composition (nature and concentration), linear (Multi Linear Regression-Ordinary Least Square "MLR-OLS" and Multi Linear Regression-Partial Least Square "MLR-PLS") and non-linear (Artificial Neural Network "ANN") models have been built. The MLR-OLS model was disqualified during the training phase since it did not show good results even in the training phase, which could not lead to effective predictions during the validation phase. Then, the performances of MLR-PLS and ANN were evaluated with validation data. Good concentration predictions were obtained in both cases for all the involved analytes. However, in the case of methane, better prediction performances were obtained with ANN, which is consistent with the fact that the MOX sensor's response to CH4 is logarithmic, whereas only linear sensor responses were obtained for the other analytes. Finally, prediction tests performed on one-year aged sensor platforms revealed that PLS model predictions on aged platforms mainly suffered from concentration offsets and that ANN predictions mainly suffered from a drop of sensitivity.

Comparative Metabolome Profiling for Revealing the Effects of Different Cooking Methods on Glutinous Rice Longjing57 (Oryza sativa L. var. Glutinosa).

Glutinous rice (GR), an important food crop in Asia, provides prolonged energy for the human body due to its high amylopectin content. The non-volatile metabolites generated by different cooking methods that affect the nutritional value and color of GR are still poorly understood. Herein, a widely targeted metabolomics approach was used to understand the effects of different cooking methods (steaming, baking, and frying) on the metabolite profiles of GR. Compared with other treatments, steamed GR had a brighter color and significantly lower contents of total sugar, starch, amylopectin, and amylose, at 40.74%, 14.13%, 9.78%, and 15.18%, respectively. Additionally, 70, 108, and 115 metabolites were significantly altered in the steaming, baking, and frying groups respectively, and amino acid and carbohydrate metabolism were identified as the representative metabolic pathways based on KEGG annotations. Further evaluation of 14 amino acids and 12 carbohydrates in steamed GR, especially 4-aminobutyric acid, suggested its high nutraceutical value. Additionally, multivariate analysis indicated that total sugar content, amylose content, beta-alanine methyl ester hydrochloride, and 4-aminobutyric acid played a critical role in color formation in raw and cooked GR. Finally, the levels of major amino acids and carbohydrates were quantified by conventional methods to verify the reliability of the metabolome. Consequently, this in-depth understanding of metabolite profiling in normal cooking methods has provided a foundation for the processing of GR products.

Amino acid profile behavior during the fermentation of Criollo cocoa beans.

The study investigated the behavior of seventeen amino acids during spontaneous (SF) and starter culture (SC) fermentation of Criollo cocoa beans from Copallín, Guadalupe and Tolopampa, Amazonas-Peru. For this purpose, liquid chromatography (UHPLC) was used to quantify amino acids. Multivariate analysis was used to differentiate the phases of the fermentation process. The percentage of essential amino acids during SC fermentation (63.4%) was higher than SF (61.8%); it was observed that the starter culture accelerated their presence and increased their concentration during the fermentation process. The multivariate analysis identified a first stage (day 0 to day 2), characterized by a low content of amino acids that increased due to protein hydrolysis. The study showed that adding the starter culture (Saccharomyces cerevisiae) to the fermentation mass increased the concentration of essential amino acids (63.0%) compared to the spontaneous process (61.8%). Moreover, this addition reduced the fermentation time (3-4 days less), demonstrating that the fermentation process with a starter culture allows obtaining a better profile of amino acids precursors of flavor and aroma.

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.

Combustion-Derived Pollutants Linked with Kidney Disease in Low-Lying Flood-Affected Areas in the Balkans.

Tens of thousands of people in southern Europe suffer from Balkan endemic nephropathy (BEN), and four times as many are at risk. Incidental ingestion of aristolochic acids (AAs), stemming from the ubiquitousAristolochia clematitis(birthwort) weed in the region, leads to DNA adduct-induced toxicity in kidney cells, the primary cause of BEN. Numerous cofactors, including toxic organics and metals, have been investigated, but all have shown small contributions to the overall BEN relative to non-BEN village distribution gradients. Here, we reveal that combustion-derived pollutants from wood and coal burning in Serbia also contaminate arable soil and test as plausible causative factors of BEN. Using a GC-MS screening method, biomass-burning-derived furfural and coal-burning-derived medium-chain alkanes were detected in soil samples from BEN endemic areas levels at up to 63-times and 14-times higher, respectively, than in nonendemic areas. Significantly higher amounts were also detected in colocated wheat grains. Coexposure studies with cultured kidney cells showed that these pollutants enhance DNA adduct formation by AA, - the cause of AA nephrotoxicity and carcinogenicity. With the coincidence of birthwort-derived AAs and the widespread practice of biomass and coal burning for household cooking and heating purposes and agricultural burning in rural low-lying flood-affected areas in the Balkans, these results implicate combustion-derived pollutants in promoting the development of BEN.

Classification of Fashion Models' Walking Styles Using Publicly Available Data, Pose Detection Technology, and Multivariate Analysis: From Past to Current Trendy Walking Styles.

Understanding past and current trends is crucial in the fashion industry to forecast future market demands. This study quantifies and reports the characteristics of the trendy walking styles of fashion models during real-world runway performances using three cutting-edge technologies: (a) publicly available video resources, (b) human pose detection technology, and (c) multivariate human-movement analysis techniques. The skeletal coordinates of the whole body during one gait cycle, extracted from publicly available video resources of 69 fashion models, underwent principal component analysis to reduce the dimensionality of the data. Then, hierarchical cluster analysis was used to classify the data. The results revealed that (1) the gaits of the fashion models analyzed in this study could be classified into five clusters, (2) there were significant differences in the median years in which the shows were held between the clusters, and (3) reconstructed stick-figure animations representing the walking styles of each cluster indicate that an exaggerated leg-crossing gait has become less common over recent years. Accordingly, we concluded that the level of leg crossing while walking is one of the major changes in trendy walking styles, from the past to the present, directed by the world's leading brands.

Long-term success and influencing factors of regenerative surgery for intra-bony defects: A retrospective cohort study.

BACKGROUND: The composite outcome measure (COM) more comprehensively assesses the clinical efficacy of regenerative surgery than a single probing measurement. We aimed to assess long-term success defined by the COM (clinical attachment level [CAL] gain of ≥3 mm and postsurgery probing pocket depth [PPD] ≤ 4 mm) and influencing factors of regenerative surgery using bone substitutes and resorbable collagen membrane (RM) for intra-bony defects (IBDs). METHODS: We retrospectively collected data from patients who underwent regenerative surgery using deproteinized bovine bone mineral (DBBM) and RM for IBDs. CAL and PPD values were compared at baseline (preoperative), 1 year (short-term), and at the last follow-up (5-10 years). Multivariate logistic regressions were performed to identify factors influencing COM-based long-term success. RESULTS: Eighty-one defects in 75 teeth of 33 patients who completed follow-up (6.5 ± 1.4 years) were included. One tooth was lost. All defects with complete follow-up exhibited long-term average CAL gain (3.00 ± 2.00 mm, 95% confidence interval [CI]: 2.56-3.44 mm, p < 0.001) and PPD reduction (2.06 ± 1.91 mm, 95% CI: 1.64-2.49 mm, p < 0.001). Long-term success was achieved in 38.8% of IBDs. CAL and PPD values were comparable between 1 year and the last follow-up. Logistic regression analyses revealed that male sex (odds ratio [OR] = 0.23, 95% CI: 0.07-0.75) and bleeding on probing (BOP) during supportive periodontal therapy (OR = 0.96, 95% CI: 0.94-0.99) were risk factors for long-term success. CONCLUSIONS: Regenerative surgery with DBBM and RM for IBDs can achieve some degree of long-term success defined by COM. However, within this study's limitations, male sex and higher BOP incidence postoperatively are negatively associated with optimal long-term success. CLINICAL TRIAL NUMBER: ChiCTR2300069016.

Limbic-predominant age-related TDP-43 encephalopathy in the oldest old: a population-based study.

Population-based cohort studies are essential for understanding the pathological basis of dementia in older populations. Previous studies have shown that limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) increases with age, but there have been only a few studies, which have investigated this entity in a population-based setting. Here we studied the frequency of LATE-NC and its associations with other brain pathologies and cognition in a population aged ≥ 85 years. The population-based Vantaa 85+ study cohort includes all 601 individuals aged ≥ 85 years who were living in Vantaa, Finland in 1991. A neuropathological examination was performed on 304 subjects (50.5%) and LATE-NC staging was possible in 295 of those. Dementia status and Mini-Mental State Examination (MMSE) scores were defined in the baseline study and 3 follow-ups (1994-99). The LATE-NC stages were determined based on TDP-43 immunohistochemistry, according to recently updated recommendations. Arteriolosclerosis was digitally assessed by calculating the average sclerotic index of five random small arterioles in amygdala and hippocampal regions, and frontal white matter. The association of LATE-NC with arteriolosclerosis and previously determined neuropathological variables including Alzheimer's disease neuropathological change (ADNC), Lewy-related pathology (LRP), hippocampal sclerosis (HS), and cerebral amyloid angiopathy (CAA), and cognitive variables were analysed by Fisher's exact test, linear and logistic regression (univariate and multivariate) models. LATE-NC was found in 189 of 295 subjects (64.1%). Stage 2 was the most common (28.5%) and stage 3 the second most common (12.9%), whereas stages 1a, 1b and 1c were less common (9.5%, 5.1% and 8.1%, respectively). Stages 1a (P < 0.01), 2 (P < 0.001) and 3 (P < 0.001) were significantly associated with dementia and lower MMSE scores. LATE-NC was associated with ADNC (P < 0.001), HS (P < 0.001), diffuse neocortical LRP (P < 0.002), and arteriolosclerosis in amygdala (P < 0.02). In most cases LATE-NC occurred in combination alongside other neuropathological changes. There were only six subjects with dementia who had LATE-NC without high levels of ADNC or LRP (2% of the cohort, 3% of the cases with dementia), and five of these had HS. In all multivariate models, LATE-NC was among the strongest independent predictors of dementia. When LATE-NC and ADNC were assessed in a multivariate model without other dementia-associated pathologies, the attributable risk was higher for LATE-NC than ADNC (24.2% vs. 18.6%). This population-based study provides evidence that LATE-NC is very common and one of the most significant determinants of dementia in the general late-life aged population.

Clinical Association Between Immune-related Adverse Events and Treatment Efficacy in Patients With Non-small-cell Lung Cancer Treated With Nivolumab-Ipilimumab-based Therapy.

BACKGROUND/AIM: Nivolumab and ipilimumab combination therapy has been extensively explored for the treatment of advanced non-small-cell lung cancer (NSCLC) through the pivotal phase III trials CheckMate 227 and CheckMate 9LA. However, the relationship between immune-related adverse events (irAEs) and the effectiveness of nivolumab plus ipilimumab-based therapy in a real-world clinical setting remains uncertain. PATIENTS AND METHODS: We performed a retrospective analysis of 28 patients with advanced or recurrent NSCLC who underwent treatment with nivolumab plus ipilimumab, with or without platinum-doublet chemotherapy, from February 2021 to January 2023. The primary objective was to elucidate the clinical association between irAEs and treatment efficacy associated with nivolumab plus ipilimumab-based therapy. RESULTS: Among the 28 patients, 22 (78.6%) experienced irAEs. The median progression-free survival (PFS) was significantly longer for patients with irAEs than for those without (p=0.0158), as was overall survival (OS) (p=0.000394). The severity of irAEs had no significant influence on PFS or OS. The objective response rate tended to be higher in patients with irAEs than in those without (50.0% versus 0.0%, respectively; p=0.0549). Multivariate analysis indicated that irAE occurrence was an independent factor for improved PFS (hazard ratio=0.2084, p=0.01383) and OS (hazard ratio=0.0857, p=0.001588). Interstitial lung disease was inferior to other irAE profiles for both PFS and OS. CONCLUSION: Patients with advanced NSCLC experiencing irAEs demonstrated superior clinical outcomes when treated with nivolumab plus ipilimumab-based therapy compared with those without irAEs. However, immune-related interstitial lung disease may be less linked with PFS and OS than other irAE profiles.

Spectroscopy-based chemometrics combined machine learning modeling predicts cashew foliar macro- and micronutrients.

Precision nutrient management in orchard crops needs precise, accurate, and real-time information on the plant's nutritional status. This is limited by the fact that it requires extensive leaf sampling and chemical analysis when it is to be done over more extensive areas like field- or landscape scale. Thus, rapid, reliable, and repeatable means of nutrient estimations are needed. In this context, lab-based remote sensing or spectroscopy has been explored in the current study to predict the foliar nutritional status of the cashew crop. Novel spectral indices (normalized difference and simple ratio), chemometric modeling, and partial least square regression (PLSR) combined machine learning modeling of the visible near-infrared hyperspectral data were employed to predict macro- and micronutrients content of the cashew leaves. The full dataset was divided into calibration (70 % of the full dataset) and validation (30 % of the full dataset) datasets. An independent validation dataset was used for the validation of the algorithms tested. The approach of spectral indices yielded very poor and unreliable predictions for all eleven nutrients. Among the chemometric models tested, the performance of the PLSR was the best, but still, the predictions were not acceptable. The PLSR combined machine learning modeling approach yielded acceptable to excellent predictions for all the nutrients except sulphur and copper. The best predictions were observed when PLSR was combined with Cubist for nitrogen, phosphorus, potassium, manganese, and zinc; support vector machine regression for calcium, magnesium, iron, copper, and boron; elastic net for sulphur. The current study showed hyperspectral remote sensing-based models could be employed for non-destructive and rapid estimation of cashew leaf macro- and micro-nutrients. The developed approach is suggested to employ within the operational workflows for site-specific and precision nutrient management of the cashew orchards.

Lipids balance as a spectroscopy marker of diabetes. Analysis of FTIR spectra by 2D correlation and machine learning analyses.

The number of people suffering from type 2 diabetes has rapidly increased. Taking into account, that elevated intracellular lipid concentrations, as well as their metabolism, are correlated with diminished insulin sensitivity, in this study we would like to show lipids spectroscopy markers of diabetes. For this purpose, serum collected from rats (animal model of diabetes) was analyzed using Fourier Transformed Infrared-Attenuated Total Reflection (FTIR-ATR) spectroscopy. Analyzed spectra showed that rats with diabetes presented higher concentration of phospholipids and cholesterol in comparison with non-diabetic rats. Moreover, the analysis of second (IInd) derivative spectra showed no structural changes in lipids. Machine learning methods showed higher accuracy for IInd derivative spectra (from 65 % to 89 %) than for absorbance FTIR spectra (53-65 %). Moreover, it was possible to identify significant wavelength intervals from IInd derivative spectra using random forest-based feature selection algorithm, which further increased the accuracy of the classification (up to 92 % for phospholipid region). Moreover decision tree based on the selected features showed, that peaks at 1016 cm-1 and 2936 cm-1 can be good candidates of lipids marker of diabetes.