Effect of apolipoprotein E ε4 and its modification by sociodemographic characteristics on cognitive measures in South Asians from LASI-DAD.

BACKGROUND: We investigated the effects of apolipoprotein E (APOE) ε4 and its interactions with sociodemographic characteristics on cognitive measures in South Asians from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD). METHODS: Linear regression was used to assess the association between APOE ε4 and global- and domain-specific cognitive function in 2563 participants (mean age 69.6 ± 7.3 years; 53% female). Effect modification by age, sex, and education were explored using interaction terms and subgroup analyses. RESULTS: APOE ε4 was inversely associated with most cognitive measures (p < 0.05). This association was stronger with advancing age for the Hindi Mental State Examination (HMSE) score (ßε4×age = -0.44, p = 0.03), orientation (ßε4×age = -0.07, p = 0.01), and language/fluency (ßε4×age = -0.07, p = 0.01), as well as in females for memory (ßε4×male = 0.17, p = 0.02) and language/fluency (ßε4×male = 0.12, p = 0.03). DISCUSSION: APOE Îµ4 is associated with lower cognitive function in South Asians from India, with a more pronounced impact observed in females and older individuals. HIGHLIGHTS: APOE Îµ4 carriers had lower global and domain-specific cognitive performance. Females and older individuals may be more susceptible to ε4 effects. For most cognitive measures, there was no interaction between ε4 and education.

Comparative Analysis of Six Complete Plastomes of Tripterospermum spp.

The Tripterospermum, comprising 34 species, is a genus of Gentianaceae. Members of Tripterospermum are mostly perennial, entwined herbs with high medicinal value and rich in iridoids, xanthones, flavonoids, and triterpenes. However, our inadequate understanding of the differences in the plastid genome sequences of Tripterospermum species has severely hindered the study of their evolution and phylogeny. Therefore, we first analyzed the 86 Gentianae plastid genomes to explore the phylogenetic relationships within the Gentianae subfamily where Tripterospermum is located. Then, we analyzed six plastid genomes of Tripterospermum, including two newly sequenced plastid genomes and four previously published plastid genomes, to explore the plastid genomes' evolution and phylogenetic relationships in the genus Tripterospermum. The Tripterospermum plastomes have a quadripartite structure and are between 150,929 and 151,350 bp in size. The plastomes of Tripterospermum encoding 134 genes were detected, including 86 protein-coding genes (CDS), 37 transfer RNA (tRNA) genes, eight ribosomal RNA (rRNA) genes, and three pseudogenes (infA, rps19, and ycf1). The result of the comparison shows that the Tripterospermum plastomes are very conserved, with the total plastome GC content ranging from 37.70% to 37.79%. In repeat sequence analysis, the number of single nucleotide repeats (A/T) varies among the six Tripterospermum species, and the identified main long repeat types are forward and palindromic repeats. The degree of conservation is higher at the SC/IR boundary. The regions with the highest divergence in the CDS and the intergenic region (IGS) are psaI and rrn4.5-rrn5, respectively. The average pi of the CDS and the IGS are only 0.071% and 0.232%, respectively, indicating that the Tripterospermum plastomes are highly conserved. Phylogenetic analysis indicated that Gentianinae is divided into two clades, with Tripterospermum as a sister to Sinogeniana. Phylogenetic trees based on CDS and CDS + IGS combined matrices have strong support in Tripterospermum. These findings contribute to the elucidation of the plastid genome evolution of Tripterospermum and provide a foundation for further exploration and resource utilization within this genus.

Artificial intelligence powered advancements in upper extremity joint MRI: A review.

Magnetic resonance imaging (MRI) is an indispensable medical imaging examination technique in musculoskeletal medicine. Modern MRI techniques achieve superior high-quality multiplanar imaging of soft tissue and skeletal pathologies without the harmful effects of ionizing radiation. Some current limitations of MRI include long acquisition times, artifacts, and noise. In addition, it is often challenging to distinguish abutting or closely applied soft tissue structures with similar signal characteristics. In the past decade, Artificial Intelligence (AI) has been widely employed in musculoskeletal MRI to help reduce the image acquisition time and improve image quality. Apart from being able to reduce medical costs, AI can assist clinicians in diagnosing diseases more accurately. This will effectively help formulate appropriate treatment plans and ultimately improve patient care. This review article intends to summarize AI's current research and application in musculoskeletal MRI, particularly the advancement of DL in identifying the structure and lesions of upper extremity joints in MRI images.

Deep learning and remote photoplethysmography powered advancements in contactless physiological measurement.

In recent decades, there has been ongoing development in the application of computer vision (CV) in the medical field. As conventional contact-based physiological measurement techniques often restrict a patient's mobility in the clinical environment, the ability to achieve continuous, comfortable and convenient monitoring is thus a topic of interest to researchers. One type of CV application is remote imaging photoplethysmography (rPPG), which can predict vital signs using a video or image. While contactless physiological measurement techniques have an excellent application prospect, the lack of uniformity or standardization of contactless vital monitoring methods limits their application in remote healthcare/telehealth settings. Several methods have been developed to improve this limitation and solve the heterogeneity of video signals caused by movement, lighting, and equipment. The fundamental algorithms include traditional algorithms with optimization and developing deep learning (DL) algorithms. This article aims to provide an in-depth review of current Artificial Intelligence (AI) methods using CV and DL in contactless physiological measurement and a comprehensive summary of the latest development of contactless measurement techniques for skin perfusion, respiratory rate, blood oxygen saturation, heart rate, heart rate variability, and blood pressure.

CreativeSeg: Semantic Segmentation of Creative Sketches.

The problem of sketch semantic segmentation is far from being solved. Despite existing methods exhibiting near-saturating performances on simple sketches with high recognisability, they suffer serious setbacks when the target sketches are products of an imaginative process with high degree of creativity. We hypothesise that human creativity, being highly individualistic, induces a significant shift in distribution of sketches, leading to poor model generalisation. Such hypothesis, backed by empirical evidences, opens the door for a solution that explicitly disentangles creativity while learning sketch representations. We materialise this by crafting a learnable creativity estimator that assigns a scalar score of creativity to each sketch. It follows that we introduce CreativeSeg, a learning-to-learn framework that leverages the estimator in order to learn creativity-agnostic representation, and eventually the downstream semantic segmentation task. We empirically verify the superiority of CreativeSeg on the recent "Creative Birds" and "Creative Creatures" creative sketch datasets. Through a human study, we further strengthen the case that the learned creativity score does indeed have a positive correlation with the subjective creativity of human. Codes are available at https://github.com/PRIS-CV/Sketch-CS.

Clinical features and possible pathogenesis of multiple evanescent white dot syndrome with different retinal diseases and events: a narrative review.

Multiple evanescent white dot syndrome (MEWDS) is a rare fundus disease, characterized by acute vision loss and visual field defects. Many previous studies have explained the possible pathogenesis and clinical features of primary MEWDS. However, as the number of reported cases increases, secondary MEWDS occurs in other related retinal diseases and injuries, exhibiting some special characteristics. The associated retinal diseases include multifocal choroiditis/punctate inner choroidopathy (MFC/PIC), acute zonal occult outer retinopathy, best vitelliform macular dystrophy, pseudoxanthoma elasticum, and ocular toxoplasmosis. The related retinal injury is laser photocoagulation, surgery, and trauma. Although primary MEWDS often have a self-limiting course, secondary MEWDS may require treatment in some cases, according to the severity of concomitant diseases and complications. Notably, MEWDS secondary to MFC/PIC that is prone to forming choroidal neovascularization and focal choroidal excavation, needs positive treatment with corticosteroids. The possible underlying pathogenesis of secondary MEWDS is the exposure of choroidal antigen after the disruption of Bruch's membrane. The MEWDS-related features in secondary MEWDS are still evanescent under most circumstances. Its prognosis and treatment depend on the severity of complications. Current studies propose that the etiology is associated with immune factors, including viral infection, inflammation in choroid and Bruch's membrane, and antigen exposure caused by retinal and/or choroidal insults. More pathogenic studies should be conducted in the future. Accurate diagnosis for secondary MEWDS could benefit patients in aspects of management and prognosis.

3D Reconstruction from a Single Sketch via View-dependent Depth Sampling.

Reconstructing a 3D shape based on a single sketch image is challenging due to the inherent sparsity and ambiguity present in sketches. Existing methods lose fine details when extracting features to predict 3D objects from sketches. Upon analyzing the 3D-to-2D projection process, we observe that the density map, characterizing the distribution of 2D point clouds, can serve as a proxy to facilitate the reconstruction process. In this work, we propose a novel sketch-based 3D reconstruction model named SketchSampler. It initiates the process by translating a sketch through an image translation network into a more informative 2D representation, which is then used to generate a density map. Subsequently, a two-stage probabilistic sampling process is employed to reconstruct a 3D point cloud: firstly, recovering the 2D points (i.e., the x and y coordinates) by sampling the density map; and secondly, predicting the depth (i.e., the z coordinate) by sampling the depth values along the ray determined by each 2D point. Additionally, we convert the reconstructed point cloud into a 3D mesh for wider applications. To reduce ambiguity, we incorporate hidden lines in sketches. Experimental results demonstrate that our proposed approach significantly outperforms other baseline methods.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

BACKGROUND: The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. OBJECTIVE: A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. METHODS: A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. RESULTS: A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. CONCLUSION: Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

ANTECEDENTES: O diagnóstico precoce da doença de Alzheimer (DA) e do comprometimento cognitivo leve (CCL) continua sendo um desafio significativo na neurologia, com métodos convencionais frequentemente limitados pela subjetividade e variabilidade na interpretação. A integração da aprendizagem profunda com a inteligência artificial (IA) na análise de imagens de ressonância magnética surge como uma abordagem transformadora, oferecendo o potencial para insights diagnósticos imparciais e altamente precisos. OBJETIVO: Uma metanálise foi projetada para analisar a precisão diagnóstica do aprendizado profundo de imagens de ressonância magnética em modelos de DA e CCL. MéTODOS: Uma metanálise foi realizada nos bancos de dados das bibliotecas PubMed, Embase e Cochrane seguindo as diretrizes Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), com foco na precisão diagnóstica do aprendizado profundo. Posteriormente, a qualidade metodológica foi avaliada por meio do checklist QUADAS-2. Medidas diagnósticas, incluindo sensibilidade, especificidade, razões de verossimilhança, razão de chances diagnósticas e área sob a curva característica de operação do receptor (area under the receiver operating characteristic curve [AUROC]) foram analisadas, juntamente com análises de subgrupo para ressonância magnética ponderada em T1 e não ponderada em T1. RESULTADOS: Um total de 18 estudos elegíveis foram identificados. O coeficiente de correlação de Spearman foi de -0,6506. A metanálise mostrou que a sensibilidade e a especificidade combinadas, a razão de verossimilhança positiva, a razão de verossimilhança negativa e a razão de chances de diagnóstico foram 0,84, 0,86, 6,0, 0,19 e 32, respectivamente. A AUROC foi de 0,92. O ponto quiescente do resumo hierárquico da característica de operação do receptor (hierarchical summary of receiver operating characteristic [HSROC]) foi 3,463. Notavelmente, as imagens de 12 estudos foram adquiridas apenas por ressonância magnética ponderada em T1, e as dos outros 6 foram obtidas apenas por ressonância magnética não ponderada em T1. CONCLUSãO: Em geral, a aprendizagem profunda da ressonância magnética para o diagnóstico de DA e CCL mostrou boa sensibilidade e especificidade e contribuiu para melhorar a precisão diagnóstica.

Bi-Directional Ensemble Feature Reconstruction Network for Few-Shot Fine-Grained Classification.

The main challenge for fine-grained few-shot image classification is to learn feature representations with higher inter-class and lower intra-class variations, with a mere few labelled samples. Conventional few-shot learning methods however cannot be naively adopted for this fine-grained setting - a quick pilot study reveals that they in fact push for the opposite (i.e., lower inter-class variations and higher intra-class variations). To alleviate this problem, prior works predominately use a support set to reconstruct the query image and then utilize metric learning to determine its category. Upon careful inspection, we further reveal that such unidirectional reconstruction methods only help to increase inter-class variations and are not effective in tackling intra-class variations. In this paper, we introduce a bi-reconstruction mechanism that can simultaneously accommodate for inter-class and intra-class variations. In addition to using the support set to reconstruct the query set for increasing inter-class variations, we further use the query set to reconstruct the support set for reducing intra-class variations. This design effectively helps the model to explore more subtle and discriminative features which is key for the fine-grained problem in hand. Furthermore, we also construct a self-reconstruction module to work alongside the bi-directional module to make the features even more discriminative. We introduce the snapshot ensemble method in the episodic learning strategy - a simple trick to further improve model performance without increasing training costs. Experimental results on three widely used fine-grained image classification datasets, as well as general and cross-domain few-shot image datasets, consistently show considerable improvements compared with other methods.

Mushroom poisoning of Panaeolus subbalteatus from Ningxia, northwest China, with species identification and tryptamine detection.

Mushroom poisoning is a significant contributor to foodborne disease outbreaks in China. This study focuses on two Panaeolus subbalteatus poisoning incidents accompanied by epidemiological investigations, species identification, and toxin detection in Ningxia, northwest China. In these two poisoning incidents, some patients exhibited gastrointestinal or neurological symptoms approximately 0.5 h after ingestion of a large amount of wild mushroom. Specifically, in Case 1, one of the three patients experienced nausea, vomiting, and numbness in the throat and limbs; in Case 2, one patient reported dizziness and an abnormal sense of direction. Through morphological and phylogenetic analyses, mushroom specimens were identified as P. subbalteatus. Psilocybin and psilocin were detected in mushroom samples, and only psilocin was detected in biological samples by liquid chromatography-triple quadrupole-linear ion trap mass spectrometry screening. The average psilocybin and psilocin contents in mushroom samples were 1532.2-1760.7 and 114.5-136.0 mg/kg (n = 3), respectively. Moreover, only psilocin was detected in blood and urine samples, with average concentrations 0.5-1.2 ng/mL (n = 3) and 2.5-3.1 ng/mL (n = 3), respectively. These findings provide technical support for managing similar incidents in the future.

FGF21-dependent alleviation of cholestasis-induced liver fibrosis by sodium butyrate.

Background: The beneficial effects of fibroblast growth factor 21 (FGF21) and sodium butyrate (NaB) on protection against cholestasis-induced liver fibrosis are not well known. This study aimed to explore the effects of FGF21 and NaB on bile duct ligation (BDL)-induced liver fibrosis. Methods: Wild-type (WT) and FGF21 knockout (KO) mice received BDL surgery for 14 days. Liver fibrosis was assessed by Masson's staining for fibrosis marker expressions at the mRNA or protein levels. Adenovirus-mediated FGF21 overexpression in the WT mice was assessed against BDL damage. BDL surgeries were performed in WT and FGF21 KO mice that were administered either phosphate-buffered saline or NaB. The effects of NaB on the energy metabolism and gut microbiota were assessed using stable metabolism detection and 16S rRNA gene sequencing. Results: BDL-induced liver fibrosis in the WT mice was accompanied by high induction of FGF21. Compared to the WT mice, the FGF21 KO mice showed more severe liver fibrosis induced by BDL. FGF21 overexpression protected against BDL-induced liver fibrosis, as proved by the decreasing α-SMA at both the mRNA and protein levels. NaB administration enhanced the glucose and energy metabolisms as well as remodeled the gut microbiota. NaB alleviated BDL-induced liver fibrosis in the WT mice but aggravated the same in FGF21 KO mice. Conclusion: FGF21 plays a key role in alleviating cholestasis-induced liver damage and fibrosis. NaB has beneficial effects on cholestasis in an FGF21-dependent manner. NaB administration can thus be a novel nutritional therapy for treating cholestasis via boosting FGF21 signaling and regulating the gut microbiota.

Activated dormant stem cells recover spermatogenesis in chemoradiotherapy-induced infertility.

Male infertility is a recognized side effect of chemoradiotherapy. Extant spermatogonial stem cells (SSCs) may act as originators for any subsequent recovery. However, which type of SSCs, the mechanism by which they survive and resist toxicity, and how they act to restart spermatogenesis remain largely unknown. Here, we identify a small population of Set domain-containing protein 4 (Setd4)-expressing SSCs that occur in a relatively dormant state in the mouse seminiferous tubule. Extant beyond high-dose chemoradiotherapy, these cells then activate to recover spermatogenesis. Recovery fails when Setd4+ SSCs are deleted. Confirmed to be of fetal origin, these Setd4+ SSCs are shown to facilitate early testicular development and also contribute to steady-state spermatogenesis in adulthood. Upon activation, chromatin remodeling increases their genome-wide accessibility, enabling Notch1 and Aurora activation with corresponding silencing of p21 and p53. Here, Setd4+ SSCs are presented as the originators of both testicular development and spermatogenesis recovery in chemoradiotherapy-induced infertility.

Extremely Large Anomalous Hall Conductivity and Unusual Axial Diamagnetism in a Quasi-1D Dirac Material La3MgBi5.

Anomalous Hall effect (AHE), one of the most important electronic transport phenomena, generally appears in ferromagnetic materials but is rare in materials without magnetic elements. Here, a study of La3MgBi5 is presented, whose band structure carries multitype Dirac fermions. Although magnetic elements are absent in La3MgBi5, the signals of AHE can be observed. In particular, the anomalous Hall conductivity is extremely large, reaching 42,356 Ω-1 cm-1 with an anomalous Hall angle of 8.8%, the largest one that has been observed in the current AHE systems. The AHE is suggested to originate from the combination of skew scattering and Berry curvature. Another unique property discovered in La3MgBi5 is the axial diamagnetism. The diamagnetism is significantly enhanced and dominates the magnetization in the axial directions, which is the result of the restricted motion of the Dirac fermion at the Fermi level. These findings not only establish La3MgBi5 as a suitable platform to study AHE and quantum transport but also indicate the great potential of 315-type Bi-based materials for exploring novel physical properties.

The role of iron-rich hydrosaline liquids in the formation of Kiruna-type iron oxide-apatite deposits.

Kiruna-type iron oxide-apatite (IOA) deposits, an important source of iron, show close associations with andesitic subvolcanic intrusions. However, the processes of ore formation and the mechanism controlling iron concentration remain uncertain. Here, we report the widespread presence of high-temperature (>800°C) water-poor multisolid hydrosaline liquid inclusions in pre- and syn-ore minerals from IOA deposits of eastern China. These inclusions consistently homogenize to a liquid phase by vapor disappearance and mostly contain 3 to 10 wt % Fe, signifying a substantial capacity for iron transportation by such hydrosaline liquids. We propose that the hydrosaline liquids were likely immiscible from the dioritic magmas with high Cl/H2O in subvolcanic settings. Subsequent reaction with host rocks and/or decompression and cooling of the hydrosaline liquids is deemed responsible for the simultaneous formation of high-temperature alteration and magnetite ores, thereby providing important insights into the distinctive characteristics of IOA deposits in shallow magmatic-hydrothermal systems.

TRPV1 analgesics disturb core body temperature via a biased allosteric mechanism involving conformations distinct from that for nociception.

Efforts on developing transient receptor potential vanilloid 1 (TRPV1) drugs for pain management have been hampered by deleterious hypo- or hyperthermia caused by TRPV1 agonists/antagonists. Here, we compared the effects of four antagonists on TRPV1 polymodal gating and core body temperature (CBT) in Trpv1+/+, Trpv1-/-, and Trpv1T634A/T634A. Neither the effect on proton gating nor drug administration route, hair coverage, CBT rhythmic fluctuations, or inflammation had any influence on the differential actions of TRPV1 drugs on CBT. We identified the S4-S5 linker region exposed to the vanilloid pocket of TRPV1 to be critical for hyperthermia associated with certain TRPV1 antagonists. PSFL2874, a TRPV1 antagonist we discovered, is effective against inflammatory pain but devoid of binding to the S4-S5 linker and inducing CBT changes. These findings implicate that biased allosteric mechanisms exist for TRPV1 coupling to nociception and CBT regulation, opening avenues for the development of non-opioid analgesics without affecting CBT.