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.

Utilizing machine learning algorithms for precise discrimination of glycosuria in fluorescence spectroscopic data.

Fluorescence spectroscopy coupled with a random forest machine learning algorithm offers a promising non-invasive approach for diagnosing glycosuria, a condition characterized by excess sugar in the urine of diabetic patients. This study investigated the ability of this method to differentiate between diabetic and healthy control urine samples. Fluorescent spectra were captured from urine samples using a Xenon arc lamp emitting light within the 200 to 950 nm wavelength range, with consistent fluorescence emission observed at 450 nm under an excitation wavelength of 370 nm. Healthy control samples were also analyzed within the same spectral range for comparison. To distinguish spectral differences between healthy and infected samples, the random forest (RF) and K-Nearest Neighbors (KNN) machine learning algorithms have been employed. These algorithms automatically recognize spectral patterns associated with diabetes, enabling the prediction of unknown classifications based on established samples. Principal component analysis (PCA) was utilized for dimensionality reduction before feeding the data to RF and KNN for classification. The model's classification performance was evaluated using 10-fold cross-validation, resulting in the proposed RF-based model achieving accuracy of 96 %, specificity of 100 %, sensitivity of 93 %, and precision of 100 %. These results suggest that the proposed method holds promise for a more convenient and potentially more accurate method for diagnosing glycosuria in diabetic patients.

Spectral Profiling (Fourier Transform Infrared Spectroscopy) and Machine Learning for the Recognition of Milk from Different Bovine Breeds.

The Podolica cattle breed is widespread in southern Italy, and its productivity is characterized by low yields and an extraordinary quality of milk and meats. Most of the milk produced is transformed into "Caciocavallo Podolico" cheese, which is made with 100% Podolica milk. Fourier Transform Infrared Spectroscopy (FTIR) is the technique that, in this research work, was applied together with machine learning to discriminate 100% Podolica milk from contamination of other Calabrian cattle breeds. The analysis on the test set produced a misclassification percentage of 6.7%. Among the 15 non-Podolica samples in the test set, 2 were misclassified and recognized as Podolica milk even though the milk was from other species. The correct classification rate improved to 100% when the same method was applied to the recognition of Podolica and Pezzata Rossa milk produced by the same farm. Furthermore, this technique was tested for the recognition of Podolica milk mixed with milk from other bovine species. The multivariate model and the respective confusion matrices obtained showed that all the 14 Podolica samples (test set) mixed with 40% non-Podolica milk were correctly classified. In addition, Pezzata Rossa milk produced by the same farm was detected as a contaminant in Podolica milk from the same farm down to concentrations as little as 5% with a 100% correct classification rate in the test set. The method described yielded higher accuracy values when applied to the discrimination of milks from different breeds belonging to the same farm. One of the reasons for this phenomenon could be linked to the elimination of the environmental variable. However, the results obtained in this work demonstrate the possibility of using FTIR to discriminate between milks from different breeds.

Real-World Data Analysis of CDK4/6 Inhibitor Therapy-A Patient-Centric Single Center Study.

BACKGROUND: The quest to comprehend the real-world efficacy of CDK4/6 inhibitors (CDKis) in breast cancer continues, as patient responses vary significantly. METHODS: This single-center retrospective study evaluated CDKi use outside the trial condition from November 2016 to May 2020. Progression-free survival (PFS), time-to-treatment failure (TTF), short-term and prolonged treatment benefit (≥4 and ≥10 months), as well as prognostic and predictive markers were assessed with Kaplan-Meier and multivariate regression analyses. RESULTS: Out of 86 identified patients, 58 (67.4%) had treatment failure of which 40 (46.5%) were due to progression. Median PFS and TTF were 12 and 8.5 months, respectively. A total of 57 (66.3%) and 42 (48.8%) patients experienced short-term and prolonged treatment benefit. Independent, significant predictors for PFS were progesterone receptor expression (HR: 0.88), multiple metastatic sites (HR: 2.56), and hepatic metastasis (HR: 2.01). Significant predictors for TTF were PR expression (HR: 0.86), multiple sites (HR: 3.29), adverse events (HR: 2.35), and diabetes (HR: 2.88). Aside from tumor biology and adverse events, treatment modifications like pausing and switching of CDKi were predictive for short-term (OR: 6.73) and prolonged (OR: 14.27) therapeutic benefit, respectively. CONCLUSIONS: These findings emphasize the importance of tailored treatment strategies, highlighting the role of PR expression, metastatic burden, and therapeutic adjustments in optimizing patient outcomes in real-world breast cancer management.

Clinical outcome and prognostic factors of T4N0M0 colon cancer after R0 resection: A retrospective study.

BACKGROUND: Paradoxically, patients with T4N0M0 (stage II, no lymph node metastasis) colon cancer have a worse prognosis than those with T2N1-2M0 (stage III). However, no previous report has addressed this issue. AIM: To screen prognostic risk factors for T4N0M0 colon cancer and construct a prognostic nomogram model for these patients. METHODS: Two hundred patients with T4N0M0 colon cancer were treated at Tianjin Medical University General Hospital between January 2017 and December 2021, of which 112 patients were assigned to the training cohort, and the remaining 88 patients were assigned to the validation cohort. Differences between the training and validation groups were analyzed. The training cohort was subjected to multivariate analysis to select prognostic risk factors for T4N0M0 colon cancer, followed by the construction of a nomogram model. RESULTS: The 3-year overall survival (OS) rates were 86.2% and 74.4% for the training and validation cohorts, respectively. Enterostomy (P = 0.000), T stage (P = 0.001), right hemicolon (P = 0.025), irregular review (P = 0.040), and carbohydrate antigen 199 (CA199) (P = 0.011) were independent risk factors of OS in patients with T4N0M0 colon cancer. A nomogram model with good concordance and accuracy was constructed. CONCLUSION: Enterostomy, T stage, right hemicolon, irregular review, and CA199 were independent risk factors for OS in patients with T4N0M0 colon cancer. The nomogram model exhibited good agreement and accuracy.

Aeluropus littoralis stress-associated protein promotes water deficit resilience in engineered durum wheat.

Global climate change-related water deficit negatively affect the growth, development and yield performance of multiple cereal crops, including durum wheat. Therefore, the improvement of water-deficit stress tolerance in durum wheat varieties in arid and semiarid areas has become imperative for food security. Herein, we evaluated the water deficiency resilience potential of two marker-free transgenic durum wheat lines (AlSAP-lines: K9.3 and K21.3) under well-watered and water-deficit stress conditions at both physiological and agronomic levels. These two lines overexpressed the AlSAP gene, isolated from the halophyte grass Aeluropus littoralis, encoding a stress-associated zinc finger protein containing the A20/AN1 domains. Under well-watered conditions, the wild-type (WT) and both AlSAP-lines displayed comparable performance concerning all the evaluated parameters. Ectopic transgene expression exerted no adverse effects on growth and yield performance of the durum wheat plants. Under water-deficit conditions, no significant differences in the plant height, leaf number, spike length, and spikelet number were observed between AlSAP-lines and WT plants. However, compared to WT, the AlSAP-lines exhibited greater dry matter production, greater flag leaf area, improved net photosynthetic rate, stomatal conductance, and water use efficiency. Notably, the AlSAP-lines displayed 25 % higher grain yield (GY) than the WT plants under water-deficit conditions. The RT-qPCR-based selected stress-related gene (TdDREB1, TdLEA, TdAPX1, and TdBlt101-2) expression analyses indicated stress-related genes enhancement in AlSAP-durum wheat plants under both well-watered and water-deficit conditions, potentially related to the water-deficit resilience. Collectively, our findings support that the ectopic AlSAP expression in durum wheat lines enhances water-deficit resilience ability, thereby potentially compensate for the GY loss in arid and semi-arid regions.

Overall Survival and Prognostic Factors in Metastatic Triple-Negative Breast Cancer: A National Cancer Database Analysis.

BACKGROUND: Metastatic triple-negative breast cancer (TNBC) is aggressive with poor median overall survival (OS) ranging from 8 to 13 months. There exists considerable heterogeneity in survival at the individual patient level. To better understand the survival heterogeneity and improve risk stratification, our study aims to identify the factors influencing survival, utilizing a large patient sample from the National Cancer Database (NCDB). METHODS: Women diagnosed with metastatic TNBC from 2010 to 2020 in the NCDB were included. Demographic, clinicopathological, and treatment data and overall survival (OS) outcomes were collected. Kaplan-Meier curves were used to estimate OS. The log-rank test was used to identify OS differences between groups for each variable in the univariate analysis. For the multivariate analysis, the Cox proportional hazard model with backward elimination was used to identify factors affecting OS. Adjusted hazard ratios and 95% confidence intervals are presented. RESULTS: In this sample, 2273 women had a median overall survival of 13.6 months. Factors associated with statistically significantly worse OS included older age, higher comorbidity scores, specific histologies, higher number of metastatic sites, presence of liver or other site metastases in those with only one metastatic site (excluding brain metastases), presence of cranial and extra-cranial metastases, lack of chemotherapy, lack of immunotherapy, lack of surgery to distant sites, lack of radiation to distant sites, and receipt of palliative treatment to alleviate symptoms. In the multivariate analysis, comorbidity score, histology, number of metastatic sites, immunotherapy, and chemotherapy had a statistically significant effect on OS. CONCLUSIONS: Through NCDB analysis, we have identified prognostic factors for metastatic TNBC. These findings will help individualize prognostication at diagnosis, optimize treatment strategies, and facilitate patient stratification in future clinical trials.

Quality by design empowered preparation of itraconazole albumin nanoparticles for prostate cancer.

The current advent explores the potential of itraconazole (ITR) in prostate cancer (PCa), by its incorporation into albumin nanoparticles (NP). ITR as a repurposed moiety has displayed tremendous potential in various cancers. However, poor aqueous solubility poses hurdles towards its clinical translation. Amorphisation of ITR was observed post-incorporation within NP matrix which could prevent its precipitation in aqueous media. ITR NP was developed using quality by design and multivariate analysis and evaluated for cellular uptake, cell proliferation inhibition and the mechanism of PCa cell inhibition. Time and concentration-dependent serum stability and hemolytic potential revealed safety of ITR NP. Morphological changes and nuclear staining studies revealed the efficacy of ITR and ITR NP in promoting growth inhibition of PC-3 cells. Superior qualitative and quantitative uptake, reactive oxygen species (ROS) and mitochondrial impairment for ITR NP in comparison with ITR and control group was observed. Cell cycle study revealed remarkable G2/M phase inhibition in PC-3 cells. ITR NP demonstrated superior anticancer potential in 3D tumoroids mimicking the micro-metastatic lesions compared to control and ITR. Hence, ITR NP can be a favorable alternative therapeutic alternative in PCa.

A comparative metabolomic investigation of different sections of Sicilian Citrus x limon (L.) Osbeck, characterization of bioactive metabolites, and evaluation of in vivo toxicity on zebrafish embryo.

Citrus fruits are a diverse and economically important group of fruit crops known for their distinctive flavors and high nutritional value. Their cultivation and consumption contribute significantly to the global agricultural economy and offer a wide range of health benefits. Among the genetic diversity of citrus species, Citrus x limon (L.) Osbeck is particularly relevant due to its chemical composition and potential health benefits. Two cultivars from the Sicily region (southern Italy) were compared for their phenolic content and preliminary antioxidant activity to select the distinctive extract with potential biological activity. A detailed characterization revealed the occurrence of phenolics, coumarins, and flavonoids. The quantification of metabolites contained in the selected extract was performed by an ultrahigh-performance liquid chromatographic method coupled with an ultraviolet detector. Different concentrations were tested in vivo through the fish embryo acute toxicity test, and the 50% lethal dose of 107,833 µg mL-1 was calculated. Finally, the effect of the extract on hatching was evaluated, and a dose-dependent relationship with the accelerated hatching rate was reported, suggesting a Femminello Zagara Bianca green peel upregulating effect on the hatching enzymes. PRACTICAL APPLICATION: Citrus fruits and their products continue to be one of the natural food sources with the highest waste output. In this study, we demonstrate how food industry waste, particularly lemon peel, is rich in bioactive compounds with anti-inflammatory and antioxidant properties that may be used in the nutraceuticals industry.

Prognostic nomogram in middle-aged and elderly patients with chordoma: A SEER-based study.

BACKGROUND: Chordoma is a bone tumor that tends to occur in middle-aged and elderly people. It grows relatively slowly but is aggressive. The prognosis of middle-aged and elderly patients with chordoma is quite different from that of young patients with chordoma. OBJECTIVES: The purpose of the research was to construct a nomogram to predict the Individualized prognosis of middle-aged and elderly (age greater than or equal to 40 years) patients with chordoma. METHODS: In this study, we screened 658 patients diagnosed with chordoma from 1983 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. We determined the independently prognostic factors that affect the survival of patients by univariate and multivariate Cox proportional hazards model. Based on the independent prognostic factors, we constructed a nomogram to predict the overall survival (OS) rates of middle-aged and elderly patients with chordoma at 3 and 5 years. The validation of this nomogram was completed by evaluating the calibration curve and the C-index. RESULTS: We screened a total of 658 patients and divided them into two cohort. Training cohort had 462 samples and validation cohort had 196 samples. The multivariate Cox proportional hazards model of the training group showed an association of age, tumor size, histology, primary site, surgery, and extent of disease with OS rates. Based on these results, we constructed the corresponding nomogram. The calibration curve and C-index showed the satisfactory ability of the nomogram in terms of predictive ability. CONCLUSION: Nomogram can be an effective prognostic tool to assess the prognosis of middle-aged and elderly patients with chordoma and can help clinicians in medical decision-making and enable patients to receive more accurate and reasonable treatment.

Evaluation of intrinsic and extrinsic factors affecting pregnancy rate in dual-purpose cows under tropical conditions.

Embryo transfer (ET) in bovines was created with the purpose of increasing the pregnancy rate (PR) of animals with high genetic value; however, multiple factors have been found to affect the success of this reproductive biotechnology. These factors are frequently grouped in intrinsic and extrinsic factors. Thus, the objective of the present experiments was to assess the effect of intrinsic and extrinsic factors on the pregnancy rate under tropical conditions. To do this a total of 648 embryo transfer (ET) procedures were performed between January and December 2021. The intrinsic factors were size and location of the corpus luteum, body condition, genetic group, age and parity; while extrinsic factors were location of the farm, environmental comfort, season in which the ET was carried out, prevailing weather conditions, and the preservation, quality, and the development stage of embryos at the time of ET. A χi2 was used for analysis of main effects, and logistic regression analysis to calculate the probability of pregnancy and the association between intrinsic or extrinsic factors; additionally, a multivariate analysis of data clusters was used to find a linkage between the effects. While recipient female age had a negative effect (Odds ratio = 0.345-0.871) on PR (p < 0.05), being higher in younger cows, the rest of the intrinsic factors did not affect the PR. The significant (p < 0.05) extrinsic factors were THI category, season of year and type of embryo preservation, showing that the highest PR (p < 0.05) was obtained in the comfort THI category, during the winter season and using fresh embryos for transfer. The clustering analysis did not show any linkage between PR and intrinsic factors, while a linkage (p < 0.05) was found with season of the year and embryo preservation as extrinsic factors. It is concluded that age of the recipient cow and environmental conditions at the time of the embryo transfer are key factors to be considered for a successful pregnancy rate from in-vitro ET programs using dual-purpose cows under tropical conditions.

Development of an evaluation and treatment strategy for olfactory neuroblastoma: a review of evidence from large-scale studies, including population-based and multicenter studies, and meta-analyses.

Olfactory neuroblastoma is a rare sinonasal malignancy arising from the olfactory epithelium that is characterized by skull base involvement and a modest natural history. Because of its rarity and long course, identification of independent prognostic factors is dependent on multivariate analysis of large, long-term data. In this review, we outline evidence for the evaluation and treatment of olfactory neuroblastoma obtained from recent large-scale population-based studies, meta-analyses and multicenter studies. Hyams grade is currently the only pathological grade system for olfactory neuroblastoma. The modified Kadish staging and Dulguerov classification are available for clinical staging. The results of large-scale studies have confirmed Hyams, the modified Kadish and Dulguerov as independent prognostic factors. Surgery followed by radiotherapy provides the best overall survival and recurrence-free survival for resectable disease. The question of whether postoperative radiotherapy should be administered for all cases or only for those at risk of recurrence remains unanswered. Exclusively endoscopic resection is indicated for modified Kadish A/B cases without any increase in the risk of death or recurrence, and is also indicated for modified Kadish C cases if a negative surgical margin is ensured. For more advanced cases, such as those with extensive brain infiltration, the open approach is indicated. Elective nodal irradiation prevents late nodal recurrence of N0 patients. Chemotherapy has failed to show a benefit in survival or disease control. Current needs for olfactory neuroblastoma include the development and validation of refined staging systems suitable for current practice; expansion of indications for endoscopic surgery; less invasive surgery; definitive radiotherapy and novel systemic therapy.

Unravelling the metabolomic diversity of pigmented and non-pigmented traditional rice from Tamil Nadu, India.

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

Comparative study on volatile compounds and taste components of various citrus cultivars using electronic sensors, GC-MS, and GC-olfactometry.

Various citrus fruits' flavor compounds were analyzed using an electronic sensor (E-sensor), and odor-active compounds were identified using gas chromatography-mass spectrometry-olfactometry (GC-MS-O). In the E-tongue analysis, the intensity of sweetness, saltiness, and bitterness was highest in Citrus unshiu, while sourness and umami were highest in C. setomi. A total of 43 volatile compounds were detected in the E-nose analysis, and the compound with the highest peak area was limonene, a type of terpenoid, which exhibited a prominent peak area in C. unshiu. Principal component analysis between flavor compounds and each sample explained a total variance of 83.15% and led to the classification of three clusters. By GC-MS-O, 32 volatile compounds were detected, with limonene being the most abundant, ranging from 20.28 to 56.21 mg/kg. The odor-active compounds were identified as (E)-2-hexenal, hexanal, α-pinene, ß-myrcene, limonene, γ-terpinene, nonanal, and D-carvone, respectively.

Comparative metabolite analysis of Piper sarmentosum organs approached by LC-MS-based metabolic profiling.

Piper sarmentosum Roxb. (Piperaceae) is a traditional medicinal and food plant widely distributed in the tropical and subtropical regions of Asia, offering both health and culinary benefits. In this study the secondary metabolites in different organs of P. sarmentosum were identified and their relative abundances were characterized. The metabolic profiles of leaves, roots, stems and fruits were comprehensively investigated by liquid chromatography high-resolution mass spectrometry (LC-HR-MS) and the data subsequently analyzed using multivariate statistical methods. Manual interpretation of the tandem mass spectrometric (MS/MS) fragmentation patterns revealed the presence of 154 tentatively identified metabolites, mostly represented by alkaloids and flavonoids. Principle component analysis and hierarchical clustering indicated the predominant occurrence of flavonoids, lignans and phenyl propanoids in leaves, aporphines in stems, piperamides in fruits and lignan-amides in roots. Overall, this study provides extensive data on the metabolite composition of P. sarmentosum, supplying useful information for bioactive compounds discovery and patterns of their preferential biosynthesis or storage in specific organs. This can be used to optimize production and harvesting as well as to maximize the plant's economic value as herbal medicine or in food applications.

Local risk factors for one-year dental implant loss and late loss in 287 failed implants caused by peri-implantitis or infection after prosthesis loading: a retrospective study.

The purpose of this paper was to retrospectively assess the local factors that are likely to be associated with the risks for one-year dental implant loss.A retrospective study was designed and implemented. The sample consisted of patients who underwent an implant loss or removal caused by peri-implantitis or infection after prosthesis loading. The chi-squared test and generalised estimating equations (GEE) were used to explore the potential risk factors for one-year implant loss. A total of 279 patients with 287 failed implants were enrolled in this study. Immediate implant placement exhibited a 3.373 (95% CI: 1.652 to 6.886) significantly increased risk to experience one-year implant loss than early and late implant placement (p = 0.001). In addition, implants loaded during a healing period fewer than two months after implant placement were at 18.139 (95% CI: 8.925 to 36.866) significantly higher risk of one-year implant loss when compared with those that loaded within more than two months after implant placement (p < 0.001). Smokers were 1.866 (OR = 1.866,95% CI: 0.993 to 3.510) times as high risk for one-year implant loss as non-smokers, but there were no significant statistical differences (p = 0.053). Immediate implant placement and early implant loading were considered risk factors for one-year implant loss.

Tumor budding is an optimal indictor of occult cervical metastasis in clinical early-stage buccal mucosa squamous cell carcinoma.

BACKGROUND: Buccal mucosa squamous cell carcinoma (BMSCC) is an aggressive disease. This study investigated the clinicopathological significance of tumor budding (TB), depth of invasion (DOI), and mode of invasion (MOI) on occult cervical metastasis (CM) of BMSCC. METHODS: Seventy-one cT1-2N0 BMSCC patients were included in this retrospective study. TB, DOI, MOI, and other clinicopathological features were reviewed. Risk factors for occult CM, locoregional recurrence-free survival (LRRFS), and overall survival (OS) were analyzed using logistic regression and Cox's proportional hazard models, respectively. RESULTS: Multivariate analysis with the logistic regression model revealed that MOI, DOI, and TB were significantly associated with occult CM in early-stage BMSCC after adjusting for variates. However, multivariate analysis with the Cox's proportional hazard model found only TB to be a prognostic factor for LRRFS (hazard ratio 15.03, 95% confidence interval [CI] 1.94-116.66; p = 0.01; trend test p = 0.03). No significant association was found between MOI, DOI, or TB and OS. CONCLUSIONS: The optimal predictor of occult CM and prognosis of early-stage BMSCC is TB, which may assist clinicians in identifying patients at high risk of cervical metastasis.

MD3F: Multivariate Distance Drift Diffusion Framework for High-Dimensional Datasets.

High-dimensional biomedical datasets have become easier to collect in the last two decades with the advent of multi-omic and single-cell experiments. These can generate over 1000 measurements per sample or per cell. More recently, focus has been drawn toward the need for longitudinal datasets, with the appreciation that important dynamic changes occur along transitions between health and disease. Analysis of longitudinal omics data comes with many challenges, including type I error inflation and corresponding loss in power when thousands of hypothesis tests are needed. Multivariate analysis can yield approaches with higher statistical power; however, multivariate methods for longitudinal data are currently limited. We propose a multivariate distance-based drift-diffusion framework (MD3F) to tackle the need for a multivariate approach to longitudinal, high-throughput datasets. We show that MD3F can result in surprisingly simple yet valid and powerful hypothesis testing and estimation approaches using generalized linear models. Through simulation and application studies, we show that MD3F is robust and can offer a broadly applicable method for assessing multivariate dynamics in omics data.

Targeted and untargeted serum NMR metabolomics to reveal initial kidney disease in diabetes mellitus.

Serum 1H NMR metabolomics has been used as a diagnostic tool for screening type 2 diabetes (T2D) with chronic kidney disease (CKD) as comorbidity. This work aimed to evaluate 1H NMR data to detect the initial kidney damage and CKD in T2D subjects, through multivariate statistical analysis. Clinical data and biochemical parameters were obtained for classifying five experimental groups using KDIGO guidelines: Control (healthy subjects), T2D, T2D-CKD-mild, T2D-CKD-moderate, and T2D-CKD-severe. Serum 1H NMR spectra were recorded to follow two strategies: one based on metabolite-to-creatinine (Met/Cr) ratios as targeted metabolomics, and the second one based on untargeted metabolomics from the 1H NMR profile. A prospective biomarkers panel of the early stage of T2D-CKD based in metabolite-to-creatinine ratio (ornithine/Cr, serine/Cr, mannose/Cr, acetate/Cr, acetoacetate/Cr, formate/Cr, and glutamate/Cr) was proposed. Later, a statistical model based on non-targeted metabolomics was used to predict initial CKD, and its metabolic pathway analysis allowed identifying the most affected pathways: phenylalanine, tyrosine, and tryptophan biosynthesis; valine, leucine, and isoleucine degradation; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; and histidine metabolism. Nonetheless, further studies with a larger cohort are advised to precise ranges in metabolite-to-creatinine ratios and evaluate the prediction pertinency to detect initial CKD in T2D patients in both statistical models proposed.

Comparative metabolite profiling of three savannic species of Banisteriopsis (Malpighiaceae) via UPLC-MS/MS and chemometric tools.

Banisteriopsis (Malpighiaceae) is an important genus of neotropical savannas with related biological and medicinal activities but under-explored metabolomic profiles. We present a chemometric analysis for discriminating secondary metabolites of three species of Banisteriopsis (B. laevifolia, B. malifolia, and B. stellaris) leaves. Initially, each species was separately extracted with ethanol:water (4:1, v/v) and analysed by Ultra Performance Liquid Chromatography coupled with Mass Spectrometry (UPLC-MS/MS). The chromatographic profiles were subjected to Global Natural Product Social (GNPS) and Partial Least Squares Discriminant Analysis (PLS-DA). Eighty-nine compounds (cosine ≥ 0.90) were annotated, including flavonoids, phenolics, and acids. The chemometric analysis (VIP Score) showed each species' relative concentration of the more relevant compounds. In addition, four compounds that discriminate the metabolomic profiles of B. laevifolia, B. malifolia, and B. stellaris were identified by PLS-DA.