Active polyvinyl alcohol films with enhanced strength, antioxidant and antibacterial properties by incorporating nanocellulose and tannin.

There is an increasing demand of food packaging materials from sustainable bio- polymers. In this study, tannin-cellulose nanocrystal (TCNCs) fillers were first prepared using dialdehyde cellulose nanocrystal (DACNCs) and tannin through the nucleophilic addition reaction, and then added to PVA matrix as reinforcement fillers to fabricate active food packaging films. FT-IR analysis confirmed the successful reaction between PVA and TCNCs.

Effect of dialdehyde nanocellulose-tannin fillers on antioxidant, antibacterial, mechanical and barrier properties of chitosan films for cherry tomato preservation.

In this study, bio-based composite films from nanocellulose, tannin and chitosan were fabricated. First, tannin was covalently immobilized onto dialdehyde CNCs (DACNCs) through the nucleophilic reaction to obtain TA-CNCs. TA-CNCs were then added into chitosan matrix as the nanofillers to obtain chitosan-TA-CNC (CS-TA-CNC) films.

Multimodal Data-Driven Segmentation of Bone Metastasis Lesions in SPECT Bone Scans Using Deep Learning.

Background: Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases due to its high sensitivity, low-cost equipment, and radiopharmaceutical. However, the low spatial resolution of SPECT scans significantly hinders manual analysis by nuclear medicine physicians.

Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

Background: Deep learning is the primary method for conducting automated analysis of SPECT bone scintigrams. The lack of available large-scale data significantly hinders the development of well-performing deep learning models, as the performance of a deep learning model is positively correlated with the size of the dataset used. Therefore, there is an urgent demand for an automated data generation method to enlarge the dataset of SPECT bone scintigrams.

Implication of Amyloid Precursor-like Protein 2 Expression in Cutaneous Squamous Cell Carcinoma Pathogenesis.

Background/aim: Regulatory functions of amyloid precursor-like protein 2 (APLP2) expression in intracellular trafficking of major histocompatibility complex class I (MHC-I) and biological behavior of tumor cells have been reported in various types of malignancies but not in cutaneous squamous cell carcinoma (CSCC). This study aimed to investigate the role of APLP2 expression in the pathogenesis of CSCC.

Patients And Methods: The expression of APLP2 and a key modulator of cancer immune escape, MHC-I, were determined in CSCC tissue samples obtained from 141 patients using immunohistochemistry.

Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities.

Human Phenotype Ontology (HPO)-based approaches have gained popularity in recent times as a tool for genomic diagnostics of rare diseases. However, these approaches do not make full use of the available information on disease and patient phenotypes. We present a new method called Phen2Disease, which utilizes the bidirectional maximum matching semantic similarity between two phenotype sets of patients and diseases to prioritize diseases and genes.

Bioinformatic analysis confirms differences in circular RNA expression profiles of cumulus cells between patients with ovarian and peritoneal endometriosis-associated infertility.

Endometriosis has a detrimental effect on oocyte quality, and ovarian endometriosis (OEM) and peritoneal endometriosis (PEM) may have different effects on female fertility. Therefore, we conducted a study to explore the circular RNA (circRNA) expression profiles of cumulus cells (CCs) in patients with OEM (n = 3), PEM (n = 3), and tubal factor infertility (TFI, n = 3) using high-throughput sequencing techniques and attempted to identify common and unique circRNAs in the OEM and PEM groups. The CIRCexplorer2 program was used to identify circRNAs.

Impact of endometriosis on embryo quality and endometrial receptivity in women undergoing assisted reproductive technology.

ART is an important treatment method for infertile patients with endometriosis. However, the effects of endometriosis on embryo quality and endometrial receptivity remain unclear. Thus, we aimed to simultaneously investigate the impact of endometriosis and its stage on embryo quality and endometrial receptivity in women undergoing ART.

Multiview Deep Forest for Overall Survival Prediction in Cancer.

Overall survival (OS) in cancer is crucial for cancer treatment. Many machine learning methods have been applied to predict OS, but there are still the challenges of dealing with multiview data and overfitting. To overcome these problems, we propose a multiview deep forest (MVDF) in this paper.

Bisphenol A exposure induces testicular oxidative damage via FTO/m6A/Nrf2 axis during postnatal development in mice.

Bisphenol A (BPA), a commonly used plasticizer in the production of polycarbonate plastics and epoxy resins, has been shown to induce male reproductive toxicity. However, the effects of BPA exposure on early testicular development have not been thoroughly studied, and the underlying mechanism is yet to be elucidated. In the current study, neonatal male mice were exposed to BPA at 0, 0.

Flexible demodulation for rotation-induced phase difference based on a phase-controlled microwave photonic filter.

High-performance demodulation of Sagnac effect is of great importance for rotation rate measurement in inertial navigation system. In this paper, we propose a flexible measurement of rotation rate based on a phase-controlled microwave photonic filter (MPF), which incorporates an orthogonal double-sideband (ODSB) modulator, a Sagnac loop, a linearly chirped fiber Bragg grating (LCFBG), a polarizer, and a photodetector. The ODSB modulator is used to generate optical carrier (OC) and first-order sidebands with mutually orthogonal polarizations.

Long non-coding RNA VAL facilitates PKM2 enzymatic activity to promote glycolysis and malignancy of gastric cancer.

Background: Gastric cancer (GC) is one of the most common types of cancer worldwide, which leads to more than 10% of cancer-related deaths. Metabolism reprogramming presents as a pivotal event in cancer initiation and progression through enhancing aerobic glycolysis and anabolic metabolism. However, the underlying regulatory mechanisms in GC remain unknown.

Detecting multiple lesions of lung cancer-caused metastasis with bone scans using a self-defined object detection model based on SSD framework.

To facilitate manual diagnosis of lung cancer-caused metastasis, in this work, we propose a deep learning-based method to automatically identify and locate the hotspots in a bone scan image which denote the lesions metastasized from lung cancer.An end-to-end metastasis lesion detection model is proposed by following the classical object detection framework single shot multibox object detector (SSD). The proposed model casts lesion detection problem into automatically learning the hierarchal representations of lesion features, locating the spatial position of lesion areas, and boxing the detected lesions.

Hormone Therapy Reduces Recurrence in Stage II-IV Uterine Low-Grade Endometrial Stromal Sarcomas: A Retrospective Cohort Study.

Low-grade endometrial stromal sarcoma (LG-ESS) is a rare and indolent malignancy. Hormone therapy has been reported as an adjuvant treatment for LG-ESS, although its effectiveness is controversial. Here we aimed to investigate the effects of postoperative hormone therapy on recurrence in patients with uterine LG-ESS.

DigestPath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system.

Examination of pathological images is the golden standard for diagnosing and screening many kinds of cancers. Multiple datasets, benchmarks, and challenges have been released in recent years, resulting in significant improvements in computer-aided diagnosis (CAD) of related diseases. However, few existing works focus on the digestive system.

Dimethyl fumarate ameliorates autoimmune hepatitis in mice by blocking NLRP3 inflammasome activation.

Dimethyl fumarate (DMF) is a fumaric acid derivative clinically approved for the treatment of some inflammatory diseases, but the underlying mechanism for its therapeutic effects remains incompletely understood. NLR family pyrin domain containing 3 (NLRP3) inflammasome activation has critical roles in innate immune responses to various infections and sterile inflammations. In this study, we aimed to explore whether DMF affects auto-immune hepatitis (AIH) in mice induced by concanavalin A (Con A) by modulating NLRP3 inflammasome activation.

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage.

Purpose: We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH).

Method: We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA).

COVID-WideNet-A capsule network for COVID-19 detection.

Ever since the outbreak of COVID-19, the entire world is grappling with panic over its rapid spread. Consequently, it is of utmost importance to detect its presence. Timely diagnostic testing leads to the quick identification, treatment and isolation of infected people.

CCT5 induces epithelial-mesenchymal transition to promote gastric cancer lymph node metastasis by activating the Wnt/β-catenin signalling pathway.

Background: Lymph node (LN) metastasis confers gastric cancer (GC) progression, poor survival and cancer-related death. Aberrant activation of Wnt/β-catenin promotes epithelial-mesenchymal transition (EMT) and LN metastasis, whereas the constitutive activation mutation of Wnt/β-catenin is rare in GC, suggesting that the underlying mechanisms enhancing Wnt/β-catenin activation need to be further investigated and understood.

Methods: Bioinformatics analyses and immunohistochemistry (IHC) were used to identify and detect LN metastasis-related genes in GC.

Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning.

Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. But some studies in recent years have shown that the prognosis of some SRCC is more favorable than other poorly differentiated adenocarcinomas, which suggests that SRCC has different degrees of biological behavior.

Enhancing Removal of Cr(VI), Pb, and Cu from Aqueous Solutions Using Amino-Functionalized Cellulose Nanocrystal.

In this work, the amino-functionalized cellulose nanocrystal (ACNC) was prepared using a green route and applied as a biosorbent for adsorption of Cr(VI), Pb, and Cu from aqueous solutions. CNC was firstly oxidized by sodium periodate to yield the dialdehyde nanocellulose (DACNC). Then, DACNC reacted with diethylenetriamine (DETA) to obtain amino-functionalized nanocellulose (ACNC) through a Schiff base reaction.

Low-Power Memristive Logic Device Enabled by Controllable Oxidation of 2D HfSe for In-Memory Computing.

Memristive logic device is a promising unit for beyond von Neumann computing systems and 2D materials are widely used because of their controllable interfacial properties. Most of these 2D memristive devices, however, are made from semiconducting chalcogenides which fail to gate the off-state current. To this end, a crossbar device using 2D HfSe is fabricated, and then the top layers are oxidized into "high-k" dielectric HfSe O via oxygen plasma treatment, so that the cell resistance can be remarkably increased.

NetGO 2.0: improving large-scale protein function prediction with massive sequence, text, domain, family and network information.

With the explosive growth of protein sequences, large-scale automated protein function prediction (AFP) is becoming challenging. A protein is usually associated with dozens of gene ontology (GO) terms. Therefore, AFP is regarded as a problem of large-scale multi-label classification.