The effect of combined pudendal nerve and spermatic cord block or caudal epidural block on postoperative analgesia after posterior urethroplasty: a randomized trial.

BACKGROUND: Postoperative pain management remains a significant challenge for patients undergoing posterior urethroplasty (PU). In a previous study, we proposed a novel technique of combined pudendal nerve (PN) and spermatic cord (SC) block to manage pain after PU. The present trial was conducted to test the hypothesis that this technique is effective for pain control after PU and provides longer-lasting analgesia than caudal epidural block (CB). METHODS: Sixty patients undergoing PU were randomized into two groups: Group NB received combined PN and SC block, and Group CB received CB. General anesthesia with a laryngeal mask was performed. The primary outcome was the postoperative analgesic duration, and the secondary outcomes included the Numeric Rating Scale (NRS) scores for pain and the number of patients with different motor scores of the lower limb at 3, 6, 12, and 24 h postoperatively. RESULTS: Two patients in Group CB were withdrawn due to block failure. The postoperative analgesic duration was statistically longer in Group NB compared with Group CB (mean difference [95% confidence interval], 115.78 min [17.80, 213.75]; P = 0.021). The NRS scores for pain at 12 and 24 h after surgery were statistically lower in Group NB compared with Group CB. Group NB had statistically more patients with motor score 0 at 3 h postoperatively than Group CB. CONCLUSIONS: PN combined with SC block is an effective technique for postoperative analgesia in PU. This technique can achieve a longer duration of analgesia and lower pain scores, especially 12 h after surgery, than a CB. TRIAL REGISTRATION: This study was registered in the Chinese Clinical Trial Register (registration no. ChiCTR2100042971, registration date on 2/2/2021).

Surgical intervention of Lemierre's syndrome: a case report and review of the literature.

BACKGROUND: Lemierre's syndrome is a fatal and rare disease that is typically characterized by oropharyngeal infection and internal jugular vein thrombosis. Timely institution of appropriate antibiotics is the standard treatment. CASE PRESENTATION: The authors report a case of Lemierre's syndrome. A 67-year-old male patient of Han ethnicity in China suffered from a large inflammatory neck mass involving left internal jugular vein thrombosis diagnosed as Lemierre's syndrome and finally cured by surgical treatment. In addition, a literature review was carried out through PubMed using the terms "Lemierre's syndrome/disease and review, meta-analysis or retrospective study" and "Lemierre's syndrome/disease and internal jugular vein". This search yielded six articles that recorded surgical methods such as drainage, craniotomy, tooth extraction, and ligation of the occluded vein to give clinicians more ideas about the treatment of the Lemierre's syndrome. CONCLUSION: This is the first review to summarize the conditions under which surgical treatment are conducted. Additionally, this is the first report of such a large inflammatory neck mass that was completely cured by surgical resection and internal jugular vein ligation. The authors also offer several conclusions regarding surgical intervention in Lemierre's syndrome for the first time.

Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression.

For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of GC outcome may rely on multiple modalities with complementary information, particularly gene expression data. Thus, there is a need to develop multimodal learning methods to enhance prediction performance. In this paper, we collect a dataset from Ruijin Hospital and propose a multimodal learning method for GC diagnosis and outcome prediction, called GaCaMML, which is featured by a cross-modal attention mechanism and Per-Slide training scheme. Additionally, we perform feature attribution analysis via integrated gradient (IG) to identify important input features. The proposed method improves prediction accuracy over the single-modal learning method on three tasks, i.e., survival prediction (by 4.9% on C-index), pathological stage classification (by 11.6% on accuracy), and lymph node classification (by 12.0% on accuracy). Especially, the Per-Slide strategy addresses the issue of a high WSI-to-patient ratio and leads to much better results compared with the Per-Person training scheme. For the interpretable analysis, we find that although WSIs dominate the prediction for most samples, there is still a substantial portion of samples whose prediction highly relies on gene expression information. This study demonstrates the great potential of multimodal learning in GC-related prediction tasks and investigates the contribution of WSIs and gene expression, respectively, which not only shows how the model makes a decision but also provides insights into the association between macroscopic pathological phenotypes and microscopic molecular features.

Artificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.

Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcome the difficulty of identifying specific cell types. In assessing data obtained from fine needle biopsies, experiments revealed that there was a higher level of agreement in the quantitative results between Artificial Intelligence (AI) algorithms and pathologists, as well as among pathologists themselves, in comparison to the data obtained from surgical specimens. We highlight that the AI-enabled analytics enhance the objectivity and interpretability of PD-L1 quantification to improve the targeted immunotherapy development in DLBCL patients.

The role of ABCC10/MRP7 in anti-cancer drug resistance and beyond.

Multidrug resistance protein 7 (MRP7), also known as ATP-binding cassette (ABC) transporter subfamily C10 (ABCC10), is an ABC transporter that was first identified in 2001. ABCC10/MRP7 is a 171 kDa protein located on the basolateral membrane of cells. ABCC10/MRP7 consists of three transmembrane domains and two nucleotide binding domains. It mediates multidrug resistance of tumor cells to a variety of anticancer drugs by increasing drug efflux and results in reducing intracellular drug accumulation. The transport substrates of ABCC10/MRP7 include antineoplastic drugs such as taxanes, vinca alkaloids, and epothilone B, as well as endobiotics such as leukotriene C4 (LTC4) and estradiol 17 ß-D-glucuronide. A variety of ABCC10/MRP7 inhibitors, including cepharanthine, imatinib, erlotinib, tariquidar, and sildenafil, can reverse ABCC10/MRP7-mediated MDR. Additionally, the presence or absence of ABCC10/MRP7 is also closely related to renal tubular dysfunction, obesity, and other diseases. In this review, we discuss: 1) Structure and functions of ABCC10/MRP7; 2) Known substrates and inhibitors of ABCC10/MRP7 and their potential therapeutic applications in cancer; and 3) Role of ABCC10/MRP7 in non-cancerous diseases.

Promising role of protein arginine methyltransferases in overcoming anti-cancer drug resistance.

Drug resistance remains a major challenge in cancer treatment, necessitating the development of novel strategies to overcome it. Protein arginine methyltransferases (PRMTs) are enzymes responsible for epigenetic arginine methylation, which regulates various biological and pathological processes, as a result, they are attractive therapeutic targets for overcoming anti-cancer drug resistance. The ongoing development of small molecules targeting PRMTs has resulted in the generation of chemical probes for modulating most PRMTs and facilitated clinical treatment for the most advanced oncology targets, including PRMT1 and PRMT5. In this review, we summarize various mechanisms underlying protein arginine methylation and the roles of specific PRMTs in driving cancer drug resistance. Furthermore, we highlight the potential clinical implications of PRMT inhibitors in decreasing cancer drug resistance. PRMTs promote the formation and maintenance of drug-tolerant cells via several mechanisms, including altered drug efflux transporters, autophagy, DNA damage repair, cancer stem cell-related function, epithelial-mesenchymal transition, and disordered tumor microenvironment. Multiple preclinical and ongoing clinical trials have demonstrated that PRMT inhibitors, particularly PRMT5 inhibitors, can sensitize cancer cells to various anti-cancer drugs, including chemotherapeutic, targeted therapeutic, and immunotherapeutic agents. Combining PRMT inhibitors with existing anti-cancer strategies will be a promising approach for overcoming anti-cancer drug resistance. Furthermore, enhanced knowledge of the complex functions of arginine methylation and PRMTs in drug resistance will guide the future development of PRMT inhibitors and may help identify new clinical indications.

A Real-world Dataset and Benchmark For Foundation Model Adaptation in Medical Image Classification.

Foundation models, often pre-trained with large-scale data, have achieved paramount success in jump-starting various vision and language applications. Recent advances further enable adapting foundation models in downstream tasks efficiently using only a few training samples, e.g., in-context learning. Yet, the application of such learning paradigms in medical image analysis remains scarce due to the shortage of publicly accessible data and benchmarks. In this paper, we aim at approaches adapting the foundation models for medical image classification and present a novel dataset and benchmark for the evaluation, i.e., examining the overall performance of accommodating the large-scale foundation models downstream on a set of diverse real-world clinical tasks. We collect five sets of medical imaging data from multiple institutes targeting a variety of real-world clinical tasks (22,349 images in total), i.e., thoracic diseases screening in X-rays, pathological lesion tissue screening, lesion detection in endoscopy images, neonatal jaundice evaluation, and diabetic retinopathy grading. Results of multiple baseline methods are demonstrated using the proposed dataset from both accuracy and cost-effective perspectives.

Hetero-Polyionic Hydrogels Enable Dendrites-Free Aqueous Zn-I2 Batteries with Fast Kinetics.

Rechargeable aqueous Zn-I2 batteries (ZIB) are regarded as a promising energy storage candidate. However, soluble polyiodide shuttling and rampant Zn dendrite growth hamper its commercial implementation. Herein, a hetero-polyionic hydrogel is designed as the electrolyte for ZIBs. On the cathode side, iodophilic polycationic hydrogel (PCH) effectively alleviates the shuttle effect and facilitates the redox kinetics of iodine species. Meanwhile, polyanionic hydrogel (PAH) toward Zn metal anode uniformizes Zn2+ flux and prevents surface corrosion by electrostatic repulsion of polyiodides. Consequently, the Zn symmetric cells with PAH electrolyte demonstrate remarkable cycling stability over 3000 h at 1 mA cm-2 (1 mAh cm-2 ) and 800 h at 10 mA cm-2 (5 mAh cm-2 ). Moreover, the Zn-I2 full cells with PAH-PCH hetero-polyionic hydrogel electrolyte deliver a low-capacity decay of 0.008 ‰ per cycle during 18 000 cycles at 8 C. This work sheds light on hydrogel electrolytes design for long-life conversion-type aqueous batteries.

Research Progress on the Pathogenesis of Knee Osteoarthritis.

Knee osteoarthritis (KOA) is a chronic joint bone disease characterized by inflammatory destruction and hyperplasia of bone. Its main clinical symptoms are joint mobility difficulties and pain, severe cases can lead to limb paralysis, which poses major pressure to the quality of life and mental health of patients, but also brings serious economic burden to society. The occurrence and development of KOA is influenced by many factors, including systemic factors and local factors. The joint biomechanical changes caused by aging, trauma and obesity, abnormal bone metabolism caused by metabolic syndrome, the effects of cytokines and related enzymes, genetic and biochemical abnormalities caused by plasma adiponectin, etc. all directly or indirectly lead to the occurrence of KOA. However, there is little literature that systematically and comprehensively integrates macro- and microscopic KOA pathogenesis. Therefore, it is necessary to comprehensively and systematically summarize the pathogenesis of KOA in order to provide a better theoretical basis for clinical treatment.

Enhanced Polysulfide Trapping and Conversion by Amorphous-Crystalline Heterostructured MnO2 Interlayers for Li-S Batteries.

The practical application of lithium-sulfur batteries (LSBs) is still hindered by several technical issues, including severe polysulfide shuttling and sluggish redox kinetics, which reduces the sulfur utilization and further results in low energy density. Herein, amorphous-crystalline heterostructured MnO2 (ACM) prepared through a simple calcination process was employed as the functional interlayer to play a double role as effective trapper and multifunctional electrocatalyst for LSBs. ACM not only combines the strong sulfur chemisorption of the amorphous MnO2 (AM) and fast Li+ transportation of the crystalline MnO2(CM) but also accelerates the interface charge transfer at the amorphous/crystalline interfaces. The LSBs with such unique interlayer exhibited an excellent rate performance of 1155.5 mAh·g-1 at 0.2 C and 692.9 mAh·g-1 at 3 C and a low decay rate of 0.071% per cycle over 500 cycles at 0.5 C. Even for a high sulfur loading of 5 mg·cm-2 at 0.1 C, a high capacity retention of 92.3% could also be achieved after 100 cycles. The concept of amorphous-crystalline heterostructures prepared by crystallization regulation might also be used for other electronic devices and catalyst designs.

Atomically Dispersed Fe-N4 and Ni-N4 Independent Sites Enable Bidirectional Sulfur Redox Electrocatalysis.

Single-atom catalysts (SACs) with high atom utilization and outstanding catalytic selectivity are useful for improving battery performance. Herein, atomically dispersed Ni-N4 and Fe-N4 dual sites coanchored on porous hollow carbon nanocages (Ni-Fe-NC) are fabricated and deployed as the sulfur host for Li-S battery. The hollow and conductive carbon matrix promotes electron transfer and also accommodates volume fluctuation during cycling. Notably, the high d band center of Fe in Fe-N4 site demonstrates strong polysulfide affinity, leading to an accelerated sulfur reduction reaction. Meanwhile, Li2S on the Ni-N4 site delivers a metallic property with high S 2p electron density of states around the Femi energy level, enabling a low sulfur evolution reaction barrier. The dual catalytic effect on Ni-Fe-NC endows sulfur cathode high energy density, prolonged lifespan, and low polarization.

A berberine induced disassembly towards zwitterionic surfactant as mimicking cell membrane for light-scattering sensing and logic devices.

A label-free light-scattering sensor for berberine determination was developed based on Gemini zwitterionic surfactant as logic devices. Amphiphilic phosphodiesters quaternary ammonium nanoparticles (PQANPs) with bionic phosphate ester structure were selected as a model for mimicking cell membrane. PQANPs self-assembled and formed the micelle structure, emitting strong light-scattering signal. Interestingly, the addition of berberine induced remarkable decrease of light-scattering attribute to its interfering behavior of PQANPs aggregation. Disassembly of PQANPs could be triggered due to electrostatic interaction and hydrophobic force between PQANPs and berberine. The berberine attached to the PQANPs surface and generated nanocomposites, resulting in significant reduce of light-scattering signal. Hence, it generated a strong light-scattering signal variation according to the change of the concentration of target. Our proposed light-scattering on-off sensor could be applied for berberine detection with detection limit of 27 nM. Moreover, a logic gate system was constructed based on PQANPs sensor with berberine and the interfering substances as the inputs and the light-scattering intensity as an output, which could hold great potential application in future clinical diagnosis and drug analysis.

Clinicopathological and molecular features of indolent natural killer-cell lymphoproliferative disorder of the gastrointestinal tract.

AIMS: Indolent natural killer (NK) cell lymphoproliferative disorder of the gastrointestinal (GI) tract (iNKLPD) is a rare, recently recognised neoplasm. Most of the reported tumours are confined to the GI tract, while a small subset of the tumours harbour JAK3 mutations. We collected four cases of iNKLPD with the goal of adding additional information to the current knowledge of this disease regarding the clinicopathological, immunohistochemical and molecular features. METHODS AND RESULTS: Similar features including medium- to large-sized lymphoid cells with variable amounts of pale or slightly eosinophilic cytoplasm, and no evidence of EBER, TCR rearrangement were found in four cases. JAK3 K563_C565del mutation was found in one of three cases that were subjected to targeted next-generation sequencing. Unique findings of our study include one iNKLPD encountered for the first time in nasopharynx, where lesions could be inadvertently diagnosed as extranodal NK/T cell lymphoma, and one iNKLPD located in the gallbladder extended deeply into muscular and adventitial layers. Exceptional CD8-positive expression was observed in one iNKLPD. In addition, positive staining of phospho-STAT5, phospho-STAT3 and phospho-p38 were found in our cases. None of the four patients received therapy for lymphoma, but all had a benign clinical outcome during a follow-up time of 20-99 months. CONCLUSIONS: We present four iNKLPDs with clinical, immunohistochemical and molecular features similar to the reported cases, as well as some unusual characters, which expand our knowledge on this disease, and further support the neoplastic nature of iNKLPDs.

Kinetic Model and Numerical Simulation of Microbial Growth, Migration, and Oil Displacement in Reservoir Porous Media.

Microbial enhanced oil recovery (MEOR) is a potential tertiary oil recovery method. However, past research has failed to describe microbial growth and metabolism reasonably, especially quantification of reaction equations and operating parameters is still not clear. The present study investigated the ability of bacteria extracted from Ansai Oilfield for MEOR. Through core flooding experiments, bacteria-treated experiments produced approximately 6.28-9.81% higher oil recovery than control experiments. Then, the microbial reaction kinetic model was established based on laboratory experimental data and mass conservation. Furthermore, the proposed model was validated by matching core flooding experiment results. Lastly, the effects of different injection parameters on bacteria growth, bacteria migration, metabolite migration, residual oil distribution, and oil recovery were studied by establishing a field-scale model. The results indicate that the injected bacteria concentration and nutrient concentration have a great influence on bacteria growth in a reservoir and the low nutrient concentration seriously restricts bacteria growth. Compared with the injected bacteria concentration, nutrient concentration has a decisive effect on bacteria and metabolite migration. The injected bacteria concentration has little effect on oil recovery, while nutrient concentration and slug volume have a significant effect on oil recovery.

Built-In Electric Field on the Mott-Schottky Heterointerface-Enabled Fast Kinetics Lithium-Sulfur Batteries.

Lithium-sulfur (Li-S) batteries (LSBs) have been considered one of the most potential candidates to substitute traditional Li-ion batteries (LIBs), owing to their high theoretical energy density and low cost. Nevertheless, the shuttle effect and the sluggish redox kinetics of lithium polysulfides (LiPSs) have long been obstacles to realizing stable LSBs with high reversible capacity. In this study, we proposed a metal-semiconductor (Mo and MoO2) heterostructure with the hollow microsphere morphology as an effective Mott-Schottky electrocatalyst to boost sulfur electrochemistry. The hollow structure can physically inhibit the shuttling of LiPSs and accommodate the volume fluctuation during cycling. More importantly, the built-in electric field at the heterointerfacial sites can effectively accelerate the reduction of LiPSs and oxidation of Li2S, thereby reaching a high sulfur utilization. With the assistance of the Mo/MoO2 catalyst, the cell exhibited prominent rate capability and stable long-term cycling performance, showing a high capacity of 630 mA h·g-1 at 4 C and a low decay of 0.073% at 1 C after 500 cycles. Even with high areal sulfur loading of 10.0 mg·cm-2, high capacity and good cycle stability were achieved at 0.2 C under lean electrolyte conditions (E/S ratio of 6 µL·mg-1).

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. We released two well-annotated benchmark datasets and organized challenges for the digestive-system pathological cell detection and tissue segmentation, in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). This paper first introduces the two released datasets, i.e., signet ring cell detection and colonoscopy tissue segmentation, with the descriptions of data collection, annotation, and potential uses. We also report the set-up, evaluation metrics, and top-performing methods and results of two challenge tasks for cell detection and tissue segmentation. In particular, the challenge received 234 effective submissions from 32 participating teams, where top-performing teams developed advancing approaches and tools for the CAD of digestive pathology. To the best of our knowledge, these are the first released publicly available datasets with corresponding challenges for the digestive-system pathological detection and segmentation. The related datasets and results provide new opportunities for the research and application of digestive pathology.

Alteration of EIF2 Signaling, Glycolysis, and Dopamine Secretion in Form-Deprived Myopia in Response to 1% Atropine Treatment: Evidence From Interactive iTRAQ-MS and SWATH-MS Proteomics Using a Guinea Pig Model.

Purpose: Atropine, a non-selective muscarinic antagonist, effectively slows down myopia progression in human adolescents and several animal models. However, the underlying molecular mechanism is unclear. The current study investigated retinal protein changes of form-deprived myopic (FDM) guinea pigs in response to topical administration of 1% atropine gel (10 g/L). Methods: At the first stage, the differentially expressed proteins were screened using fractionated isobaric tags for a relative and absolute quantification (iTRAQ) approach, coupled with nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) (n = 24, 48 eyes) using a sample pooling technique. At the second stage, retinal tissues from another cohort with the same treatment (n = 12, 24 eyes) with significant ocular changes were subjected to label-free sequential window acquisition of all theoretical mass spectra (SWATH-MS) proteomics for orthogonal protein target confirmation. The localization of Alpha-synuclein was verified using immunohistochemistry and confocal imaging. Results: A total of 1,695 proteins (8,875 peptides) were identified with 479 regulated proteins (FC ≥ 1.5 or ≤0.67) found from FDM eyes and atropine-treated eyes receiving 4-weeks drug treatment using iTRAQ-MS proteomics. Combining the iTRAQ-MS and SWATH-MS datasets, a total of 29 confident proteins at 1% FDR were consistently quantified and matched, comprising 12 up-regulated and 17 down-regulated proteins which differed between FDM eyes and atropine treated eyes (iTRAQ: FC ≥ 1.5 or ≤0.67, SWATH: FC ≥ 1.4 or ≤0.71, p-value of ≤0.05). Bioinformatics analysis using IPA and STRING databases of these commonly regulated proteins revealed the involvement of the three commonly significant pathways: EIF2 signaling; glycolysis; and dopamine secretion. Additionally, the most significantly regulated proteins were closely connected to Alpha-synuclein (SNCA). Using immunostaining (n = 3), SNCA was further confirmed in the inner margin of the inner nuclear layer (INL) and spread throughout the inner plexiform layer (IPL) of the retina of guinea pigs. Conclusion: The molecular evidence using next-generation proteomics (NGP) revealed that retinal EIF2 signaling, glycolysis, and dopamine secretion through SNCA are implicated in atropine treatment of myopia in the FDM-induced guinea pig model.

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. Therefore, we need to find a histological stratification that can predict the biological behavior of SRCC. Some studies indicate that the morphological status of cells can be linked to the invasiveness potential of cells, however, the traditional histopathological examination can not objectively define and evaluate them. Recent improvements in biomedical image analysis using deep learning (DL) based neural networks could be exploited to identify and analyze SRCC. In this study, we used DL to identify each cancer cell of SRCC in whole slide images (WSIs) and quantify their morphological characteristics and atypia. Our results show that the biological behavior of SRCC can be predicted by quantifying the morphology of cancer cells by DL. This technique could be used to predict the biological behavior and may change the stratified treatment of SRCC.