Generalized linear models (GLMs) are an integral tool in ecology. Like general linear models, GLMs assume linearity, which entails a linear relationship between independent and dependent variables. However, because this assumption acts on the link rather than the natural scale in GLMs, it is more easily overlooked. We reviewed recent ecological literature to quantify the use of linearity. We then used two case studies to confront the linearity assumption via two GLMs fit to empirical data. In the first case study we compared GLMs to generalized additive models (GAMs) fit to mammal relative abundance data. In the second case study we tested for linearity in occupancy models using passerine point-count data. We reviewed 162 studies published in the last 5 years in five leading ecology journals and found less than 15% reported testing for linearity. These studies used transformations and GAMs more often than they reported a linearity test. In the first case study, GAMs strongly out-performed GLMs as measured by AIC in modeling relative abundance, and GAMs helped uncover nonlinear responses of carnivore species to landscape development. In the second case study, 14% of species-specific models failed a formal statistical test for linearity. We also found that differences between linear and nonlinear (i.e., those with a transformed independent variable) model predictions were similar for some species but not for others, with implications for inference and conservation decision-making. Our review suggests that reporting tests for linearity are rare in recent studies employing GLMs. Our case studies show how formally comparing models that allow for nonlinear relationships between the dependent and independent variables has the potential to impact inference, generate new hypotheses, and alter conservation implications. We conclude by suggesting that ecological studies report tests for linearity and use formal methods to address linearity assumption violations in GLMs.

BACKGROUND: Prediabetes is a condition preceding the development of diabetes and is associated with an increased risk of a number of complications. The primary mode of management is thought to be lifestyle modification. Pharmacological therapy, such as glucagon-like peptide-1 receptor agonists (GLP-1RAs), were not well addressed in the literature and were only evaluated in trials as secondary and exploratory outcomes with a limited sample size. Here, GLP-1RAs are evaluated as a comprehensive therapy approach for patients with prediabetes.
METHODS: A comprehensive search of Web of Science, SCOPUS, PubMed, and Cochrane was performed on May 5, 2023, to retrieve randomized controlled trials (RCTs) comparing the effect of GLP-1RAs to placebo and/or lifestyle modification on prediabetes reversion to normoglycemia, prevention of overt diabetes, glycemic control, anthropometric parameters, and lipid profiles. Review Manager (RevMan) version 5.4 was used. The quality of RCTs was assessed using the revised version of the Cochrane Risk of Bias Tool. GRADE was performed to evaluate the certainty of evidence.
RESULTS: Twelve trials involving 2903 patients in the GLP-1RAs group and 1413 in the control group were included in the meta-analysis. Low quality of evidence revealed that GLP-1RAs significantly increased the incidence of prediabetes reversion to the normoglycemic state [RR = 1.76, 95% CI (1.45, 2.13), P < 0.00001] and moderate quality of evidence showed that GLP-1RAs significantly prevented new-onset diabetes [RR = 0.28, 95% CI (0.19, 0.43), P < 0.00001]. Significant reductions in HbA1c, fasting plasma glucose, body weight, waist circumference, triglycerides, and LDL were observed in the GLP-1RAs arm (P < 0.05). However, higher incidences of gastrointestinal disorders were reported in the GLP-1RAs group (P < 0.05).
CONCLUSIONS: GLP-1RAs combined with lifestyle modification proved to be a more effective therapy for managing prediabetic patients than lifestyle modification alone, with a tolerable safety profile. Future guidelines should consider GLP-1RAs as an adjunct to lifestyle modification in the management of prediabetic patients to provide better management and improve treatment adherence.

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.

Postural change from supine or sitting to standing up leads to displacement of 300 to 1000 mL of blood from the central parts of the body to the lower limb, which causes a decrease in venous return to the heart, hence decrease in cardiac output, causing a drop in blood pressure. This may lead to falling down, syncope, and in general reducing the quality of daily activities, especially in the elderly and anyone suffering from nervous system disorders such as Parkinson\'s or orthostatic hypotension (OH). Among different modalities to study brain function, functional near-infrared spectroscopy (fNIRS) is a neuroimaging method that optically measures the hemodynamic response in brain tissue. Concentration changes in oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (HHb) are associated with brain neural activity. fNIRS is significantly more tolerant to motion artifacts compared to fMRI, PET, and EEG. At the same time, it is portable, has a simple structure and usage, is safer, and much more economical. In this article, we systematically reviewed the literature to examine the history of using fNIRS in monitoring brain oxygenation changes caused by sudden changes in body position and its relationship with the blood pressure changes. First, the theory behind brain hemodynamics monitoring using fNIRS and its advantages and disadvantages are presented. Then, a study of blood pressure variations as a result of postural changes using fNIRS is described. It is observed that only 58 % of the references concluded a positive correlation between brain oxygenation changes and blood pressure changes. At the same time, 3 % showed a negative correlation, and 39 % did not show any correlation between them.

暂无摘要。

BACKGROUND: Uterine leiomyomas are hormone-dependent benign tumors and often begin to shrink after menopause due to the reduction in ovarian steroids. The influence of pregnancy on uterine leiomyomas size remains unclear. Here, we present a case of spontaneous regression of a giant uterine leiomyoma after delivery.
METHODS: A 40-year-old woman presented with multiple uterine leiomyomas, one of which is a giant uterine leiomyomas (approximately 8 cm in diameter) that gradually shrinked after delivery. At over two months postpartum, the large myometrial leiomyoma had transformed into a submucosal leiomyoma, and over 3 years postpartum, both the submucosal leiomyoma and multiple intramural leiomyomas completely regressed.
CONCLUSIONS: Spontaneous regression of a giant uterine leiomyom is rare after delivery. Considering uterine leiomyoma regression until over 3 year postpartum,we need to observe the regression of uterine fibroid for a longer time postpartum in the absence of fibroid related complications. In addition, it will provide new insights for treatment options of uterine leiomyomas in the future.

It is possible to identify unruptured intracranial aneurysms (UIA) using machine learning (ML) algorithms, which can be a life-saving strategy, especially in high-risk populations. To better understand the importance and effectiveness of ML algorithms in practice, a systematic review and meta-analysis were conducted to predict cerebral aneurysm rupture risk. PubMed, Scopus, Web of Science, and Embase were searched without restrictions until March 20, 2023. Eligibility criteria included studies that used ML approaches in patients with cerebral aneurysms confirmed by DSA, CTA, or MRI. Out of 35 studies included, 33 were cohort, and 11 used digital subtraction angiography (DSA) as their reference imaging modality. Middle cerebral artery (MCA) and anterior cerebral artery (ACA) were the commonest locations of aneurysmal vascular involvement-51% and 40%, respectively. The aneurysm morphology was saccular in 48% of studies. Ten of 37 studies (27%) used deep learning techniques such as CNNs and ANNs. Meta-analysis was performed on 17 studies: sensitivity of 0.83 (95% confidence interval (CI), 0.77-0.88); specificity of 0.83 (95% CI, 0.75-0.88); positive DLR of 4.81 (95% CI, 3.29-7.02) and the negative DLR of 0.20 (95% CI, 0.14-0.29); a diagnostic score of 3.17 (95% CI, 2.55-3.78); odds ratio of 23.69 (95% CI, 12.75-44.01). ML algorithms can effectively predict the risk of rupture in cerebral aneurysms with good levels of accuracy, sensitivity, and specificity. However, further research is needed to enhance their diagnostic performance in predicting the rupture status of IA.

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there\'s no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

UNASSIGNED: Unwarranted extended length of stay (LOS) increases the risk of hospital-acquired complications, morbidity, and all-cause mortality and needs to be recognized and addressed proactively.
UNASSIGNED: This systematic review aimed to identify validated prediction variables and methods used in tools that predict the risk of prolonged LOS in all hospital admissions and specifically General Medicine (GenMed) admissions.
UNASSIGNED: LOS prediction tools published since 2010 were identified in five major research databases. The main outcomes were model performance metrics, prediction variables, and level of validation. Meta-analysis was completed for validated models. The risk of bias was assessed using the PROBAST checklist.
UNASSIGNED: Overall, 25 all admission studies and 14 GenMed studies were identified. Statistical and machine learning methods were used almost equally in both groups. Calibration metrics were reported infrequently, with only 2 of 39 studies performing external validation. Meta-analysis of all admissions validation studies revealed a 95% prediction interval for theta of 0.596 to 0.798 for the area under the curve. Important predictor categories were co-morbidity diagnoses and illness severity risk scores, demographics, and admission characteristics. Overall study quality was deemed low due to poor data processing and analysis reporting.
UNASSIGNED: To the best of our knowledge, this is the first systematic review assessing the quality of risk prediction models for hospital LOS in GenMed and all admissions groups. Notably, both machine learning and statistical modeling demonstrated good predictive performance, but models were infrequently externally validated and had poor overall study quality. Moving forward, a focus on quality methods by the adoption of existing guidelines and external validation is needed before clinical application.
UNASSIGNED: https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021272198.

Giant cell tumor (GCT) of the bone is a locally aggressive primary bone tumor, that can rarely metastasize. Arising mostly in epiphysis of the long bones in young adults, the tumor is composed of mononuclear cells that are admixed with osteoclastic giant cells(OLGCs), which express RANK ligand and RANK respectively. Denosumab a monoclonal antibody against RANK ligand has been shown to reduce the tumor by causing bone lysis by inhibiting RANKL. Histological changes in 11 patients of GCT who were treated with denosumab are presented here.
Clinical records and slides of 11 patients of GCT who had been administered neoadjuvant denosumab were included in the study. Evaluation of pre and post therapy GCT specimens was performed by two pathologists (RK and VM). There were 4 males and 7 females. Their mean age was 30 years. All the patients received 120 mg denosumab subcutaneously every week with additional 120 mg on days 8 and 15 of therapy. The histological slides were reviewed and following points noted: 1) degree of ossification,2) fibrosis,3) loss of osteoclastic giant cells,4) proliferation of mononuclear cells,5) atypia,6) Permeation of osteoid by malignant cells.
Out of 11 cases, 2 cases did not show any significant histological improvement. 7 cases showed reduction in giant cells, increased fibrosis, enhanced mononuclear cell proliferation and ossification consistent with a pathological response. Atypia and osteoid permeation were noted in 2 cases which showed transformation to osteosarcoma.
Denosumab treated giant cell tumor show dramatic histological changes. The post therapy lesions may bear no resemblance to pretherapy lesion. There may be complete resolution or may be confused with benign or malignant lesions Rarely they may show sarcomatous transformation. It is imperative that the pathologist is aware of these changes to prevent diagnostic pitfalls as it poses therapeutic and prognostic implications.