Noble metal-free CZTS electrocatalysis: synergetic characteristics and emerging applications towards water splitting reactions.

This review provides a comprehensive overview of the production and modification of CZTS nanoparticles (NPs) and their application in electrocatalysis for water splitting. Various aspects, including surface modification, heterostructure design with carbon nanostructured materials, and tunable electrocatalytic studies, are discussed. A key focus is the synthesis of small CZTS nanoparticles with tunable reactivity, emphasizing the sonochemical method's role in their formation. Despite CZTS's affordability, it often exhibits poor hydrogen evolution reaction (HER) behavior. Carbon materials like graphene, carbon nanotubes, and C60 are highlighted for their ability to enhance electrocatalytic activity due to their unique properties. The review also discusses the amine functionalization of graphene oxide/CZTS composites, which enhances overall water splitting performance. Doping with non-noble metals such as Fe, Co., and Ni is presented as an effective strategy to improve catalytic activity. Additionally, the synthesis of heterostructures consisting of CZTS nanoparticles attached to MoS2-reduced graphene oxide (rGO) hybrids is explored, showing enhanced HER activity compared to pure CZTS and MoS2. The growing demand for energy and the need for efficient renewable energy sources, particularly hydrogen generation, are driving research in this field. The review aims to demonstrate the potential of CZTS-based electrocatalysts for high-performance and cost-effective hydrogen generation with low environmental impact. Vacuum-based and non-vacuum-based methods for fabricating CZTS are discussed, with a focus on simplicity and efficiency. Future developments in CZTS-based electrocatalysts include enhancing activity and stability, improving charge transfer mechanisms, ensuring cost-effectiveness and scalability, increasing durability, integrating with renewable energy sources, and gaining deeper insight into reaction processes. Overall, CZTS-based electrocatalysts show great promise for sustainable hydrogen generation, with ongoing research focused on improving performance and advancing their practical applications.

A Multimodal Generative AI Copilot for Human Pathology.

The field of computational pathology[1,2] has witnessed remarkable progress in the development of both task-specific predictive models and task-agnostic self-supervised vision encoders[3,4]. However, despite the explosive growth of generative artificial intelligence (AI), there has been limited study on building general purpose, multimodal AI assistants and copilots[5] tailored to pathology. Here we present PathChat, a vision-language generalist AI assistant for human pathology. We build PathChat by adapting a foundational vision encoder for pathology, combining it with a pretrained large language model and finetuning the whole system on over 456,000 diverse visual language instructions consisting of 999,202 question-answer turns. We compare PathChat against several multimodal vision language AI assistants and GPT4V, which powers the commercially available multimodal general purpose AI assistant ChatGPT-4[7]. PathChat achieved state-of-the-art performance on multiple-choice diagnostic questions from cases of diverse tissue origins and disease models. Furthermore, using open-ended questions and human expert evaluation, we found that overall PathChat produced more accurate and pathologist-preferable responses to diverse queries related to pathology. As an interactive and general vision-language AI Copilot that can flexibly handle both visual and natural language inputs, PathChat can potentially find impactful applications in pathology education, research, and human-in-the-loop clinical decision making.

Patient-Derived Conditionally Reprogrammed Cells in Prostate Cancer Research.

Prostate cancer (PCa) remains a leading cause of mortality among American men, with metastatic and recurrent disease posing significant therapeutic challenges due to a limited comprehension of the underlying biological processes governing disease initiation, dormancy, and progression. The conventional use of PCa cell lines has proven inadequate in elucidating the intricate molecular mechanisms driving PCa carcinogenesis, hindering the development of effective treatments. To address this gap, patient-derived primary cell cultures have been developed and play a pivotal role in unraveling the pathophysiological intricacies unique to PCa in each individual, offering valuable insights for translational research. This review explores the applications of the conditional reprogramming (CR) cell culture approach, showcasing its capability to rapidly and effectively cultivate patient-derived normal and tumor cells. The CR strategy facilitates the acquisition of stem cell properties by primary cells, precisely recapitulating the human pathophysiology of PCa. This nuanced understanding enables the identification of novel therapeutics. Specifically, our discussion encompasses the utility of CR cells in elucidating PCa initiation and progression, unraveling the molecular pathogenesis of metastatic PCa, addressing health disparities, and advancing personalized medicine. Coupled with the tumor organoid approach and patient-derived xenografts (PDXs), CR cells present a promising avenue for comprehending cancer biology, exploring new treatment modalities, and advancing precision medicine in the context of PCa. These approaches have been used for two NCI initiatives (PDMR: patient-derived model repositories; HCMI: human cancer models initiatives).

Dental caries, oral hygiene status and deleterious habits among migrant construction workers of Belagavi, India.

Background: Occupation significantly influences oral health, with factors like the work environment, stress levels, access to dental care, and job-related habits playing crucial roles. The oral health of construction workers, especially migrant workers, is a noteworthy concern. Understanding the oral health of this population is crucial for enhancing their quality of life through various means. This study aimed to investigate the prevalence of dental caries, oral hygiene status, and deleterious habits in this occupational group of Belagavi district, Karnataka. Materials and methods: Study design was cross-sectional in nature. Before commencement of the study a pilot study was conducted. Multi-stage random sampling technique was employed, and 610 participants were recruited for the study. Trained and calibrated examiners recorded WHO dentition status and treatment needs (2013) and Oral Hygiene Index Simplified (OHI-S). Collected data was analyzed using descriptive analysis, chi-square, one-way ANOVA, and multiple linear regression analysis. Results: The prevalence of dental caries among construction workers was significantly high (81%), and poor oral hygiene was observed among 36.9% of them. The prevalence of smoking, the tobacco chewing habit, and alcohol consumption among the construction workers was found to be 21.6%, 59.9%, and 37.3%, respectively. The dependence of OHI-S and DMFT on predictors (age, gender and deleterious habits) was found to be 21.5% and 39.6%, respectively. Conclusions: Migrant construction workers in Belagavi had a high caries prevalence, poor oral hygiene status, and a high prevalence of deleterious habits such as tobacco use. These results emphasize the necessity of awareness and dental health education programs to improve the oral health of construction workers.

Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Validation of prostate and breast cancer detection artificial intelligence algorithms for accurate histopathological diagnosis and grading: a retrospective study with a Japanese cohort.

Prostate and breast cancer incidence rates have been on the rise in Japan, emphasising the need for precise histopathological diagnosis to determine patient prognosis and guide treatment decisions. However, existing diagnostic methods face numerous challenges and are susceptible to inconsistencies between observers. To tackle these issues, artificial intelligence (AI) algorithms have been developed to aid in the diagnosis of prostate and breast cancer. This study focuses on validating the performance of two such algorithms, Galen Prostate and Galen Breast, in a Japanese cohort, with a particular focus on the grading accuracy and the ability to differentiate between invasive and non-invasive tumours. The research entailed a retrospective examination of 100 consecutive prostate and 100 consecutive breast biopsy cases obtained from a Japanese institution. Our findings demonstrated that the AI algorithms showed accurate cancer detection, with AUCs of 0.969 and 0.997 for the Galen Prostate and Galen Breast, respectively. The Galen Prostate was able to detect a higher Gleason score in four adenocarcinoma cases and detect a previously unreported cancer. The two algorithms successfully identified relevant pathological features, such as perineural invasions and lymphovascular invasions. Although further improvements are required to accurately differentiate rare cancer subtypes, these findings highlight the potential of these algorithms to enhance the precision and efficiency of prostate and breast cancer diagnosis in Japan. Furthermore, this validation paves the way for broader adoption of these algorithms as decision support tools within the Asian population.

Performance Analysis of Leica Biosystems Monoclonal Antibody Programmed Cell Death Ligand 1 Clone 73-10 on Breast, Colorectal, and Hepatocellular Carcinomas.

Programmed cell death receptor 1/Programmed cell death ligand 1 (PD-L1) checkpoint pathway is responsible for the control of immune cell responses. Immunotherapy using checkpoint inhibitors, such as anti-PD-L1 therapy, aids disease management and potentiates clinical outcomes. This study aimed to analyze the performance of the Leica Biosystems (LBS) USA FDA class I in vitro diagnostic monoclonal antibody (clone 73-10) to detect PD-L1 expression in breast, colorectal, and hepatocellular carcinomas compared with the class III FDA-approved PD-L1 detecting antibodies [SP263 (Ventana), 22C3 (Dako), and 28-8 (Dako)] using 208 unique tissue microarray-based cases for each tumor type. The interassay concordances between LBS 73-10 clone and other PD-L1 antibodies ranged from 0.59 to 0.95 Cohen kappa coefficient (K) and from 0.66 to 0.90 (K) for cutoff values of 1% and 50% tumor proportion score (TPS), respectively. The 73-10 clones showed inter-pathologist agreements ranging from 0.53 to 1.0 (K) and 0.34 to 0.94 (K) for cutoff values of 1% and 50% TPS, respectively. For the immune cell proportion score (IPS) using a cutoff of 1%, the Kappa coefficient of interassay concordances and inter-pathologist agreements ranged from 0.34 to 0.94. The 73-10 clone assay's sensitivity ranged from 78.3% to 100% (TPS ≥1%), 100% (TPS ≥50%), and 77.4% to 93.5% (IPS ≥1%), while its specificity was 97.9% to 100% (TPS ≥1%), 99.5% to 99.8% (TPS ≥50%), and 97.9% to 100% (IPS ≥1%). This exploratory evaluation of LBS 73-10 monoclonal antibody on a large set of breast, colorectal, and hepatocellular carcinomas showed the assay's technical performance is comparable to the FDA-approved companion/complementary diagnostics PD-L1 detection assays.

Analyzing Joshimath's sinking: causes, consequences, and future prospects with remote sensing techniques.

The Himalayas are highly susceptible to various natural disasters, such as the tectonically induced land deformation, earthquakes, landslides, and extreme climatic events. Recently, the Joshimath town witnessed a significantly large land subsidence activity. The phenomenon resulted in the development of large cracks in roads and in over 868 civil structures, posing a significant risk to inhabitants and infrastructure of the area. This study uses a time-series synthetic aperture radar (SAR) interferometry-based PSInSAR approach to monitor land deformation utilizing multi-temporal Sentinel-1 datasets. The line of sight (LOS) land deformation velocity for the Joshimath region, calculated for the year 2022-2023 using a PSInSAR-based approach, varies from - 89.326 to + 94.46 mm/year. The + ve sign indicates the LOS velocity/displacement away from the SAR sensor, whereas - ve sign signifies the earth's movement towards the SAR sensor in the direction of LOS. In addition, the study investigates feature tracking land displacement analysis using multi-temporal high-resolution Planet datasets. The result of this analysis is consistent with the PSInSAR results. The study also estimated the land deformation for the periods 2016-2017, 2018-2019, and 2020-2021 separately. Our results show that the Joshimath region experienced the highest land deformation during the year 2022-2023. During this period, the maximum land subsidence was observed in the north-western part of the town. The maximum LOS land deformation velocity + 60.45 mm/year to + 94.46 mm/year (2022-2023), occurred around Singhdwar, whereas the north and central region of the Joshimath town experienced moderate to high subsidence of the order of + 10.45 mm/year to + 60.45 mm/year (2022-2023), whereas the south-west part experienced an expansion of the order of 84.65 mm/year to - 13.13 mm/year (2022-2023). Towards the south-east, the town experienced rapid land subsidence, - 13.13 mm/year to - 5 mm/year (2022-2023). The study analyzes the causative factors of the observed land deformation in the region. Furthermore, this work assesses the ground conditions of the Joshimath region using UAV datasets acquired in the most critically affected areas such as Singhdhaar, Hotel Mountain View, Malhari Hotel, and Manoharbagh. Finally, the study provides recommendations and future prospects for the development policies that need to be adopted in the critical Himalayan regions susceptible to land deformation. The study suggests that land deformation in the region is primarily attributed to uncontrolled anthropogenic activities, infrastructural development, along with inadequate drainage systems.

The current state of digital cytology and artificial intelligence (AI): global survey results from the American Society of Cytopathology Digital Cytology Task Force.

INTRODUCTION: The integration of whole slide imaging (WSI) and artificial intelligence (AI) with digital cytology has been growing gradually. Therefore, there is a need to evaluate the current state of digital cytology. This study aimed to determine the current landscape of digital cytology via a survey conducted as part of the American Society of Cytopathology (ASC) Digital Cytology White Paper Task Force. MATERIALS AND METHODS: A survey with 43 questions pertaining to the current practices and experiences of WSI and AI in both surgical pathology and cytology was created. The survey was sent to members of the ASC, the International Academy of Cytology (IAC), and the Papanicolaou Society of Cytopathology (PSC). Responses were recorded and analyzed. RESULTS: In total, 327 individuals participated in the survey, spanning a diverse array of practice settings, roles, and experiences around the globe. The majority of responses indicated there was routine scanning of surgical pathology slides (n = 134; 61%) with fewer respondents scanning cytology slides (n = 150; 46%). The primary challenge for surgical WSI is the need for faster scanning and cost minimization, whereas image quality is the top issue for cytology WSI. AI tools are not widely utilized, with only 16% of participants using AI for surgical pathology samples and 13% for cytology practice. CONCLUSIONS: Utilization of digital pathology is limited in cytology laboratories as compared to surgical pathology. However, as more laboratories are willing to implement digital cytology in the near future, the establishment of practical clinical guidelines is needed.

Effectiveness of spaced repetition learning using a mobile flashcard application among dental students: A randomized controlled trial.

BACKGROUND: Dental education in India predominantly relies on traditional lecture-based learning (LBL), which may hinder student engagement and learning outcomes. To address these limitations, innovative learning methodologies, such as spaced repetition learning (SRL), are imperative. SRL prioritizes active recall and can enhance long-term knowledge retention. This study aims to assess the effectiveness of SRL delivered through a mobile flashcard application, in enhancing knowledge retention among dental undergraduates. METHODS: This single-blind randomized controlled trial (CTRI/2023/10/059347), conducted in Belagavi, India, involved 90 dental students who were equally distributed into control (LBL) and test (lecture followed by SRL demonstration) groups after randomization. Rigorous expert review ensured the quality of PowerPoint presentation and mobile flashcard contents. Knowledge assessments were conducted at baseline, first, and third months using a validated and reliable questionnaire. A perception survey on learning techniques was administered after the first month. Analysis methods included descriptive analysis, Pearson's chi-square test, independent t-test, and repeated measures ANOVA with Bonferroni's post hoc test. RESULTS: The pre- and post-intervention knowledge showed no significant differences, but the SRL group exhibited significantly higher retention at both first month (p ≤ 0.001) and third months (p ≤ 0.001) than the LBL group. Repeated measures ANOVA revealed significant pairwise differences in mean knowledge scores in SRL group. Students had significantly favorable perception toward SRL than LBL group. CONCLUSION: SRL delivered through mobile flashcards significantly enhances knowledge retention compared to LBL among dental students. Positive student perceptions support SRL's integration into dental curricula, with implications for improving knowledge retention among them.

Single-cell Atlas of Penile Cancer Reveals TP53 Mutations as a Driver of an Aggressive Phenotype, Irrespective of Human Papillomavirus Status, and Provides Clues for Treatment Personalization.

BACKGROUND AND OBJECTIVE: TP53 loss-of-function (TP53LOF) mutations might be a driver of poor prognosis and chemoresistance in both human papillomavirus (HPV)-independent (HPV-) and HPV-associated (HPV+) penile squamous cell carcinoma (PSCC). Here, we aim to describe transcriptomic differences in the PSCC microenvironment stratified by TP53LOF and HPV status. METHODS: We used single-cell RNA sequencing (scRNA-seq) and T-cell receptor sequencing to obtain a comprehensive atlas of the cellular architecture of PSCC. TP53LOF and HPV status were determined by targeted next-generation sequencing and sequencing HPV-DNA reads. Six HPV+ TP53 wild type (WT), six HPV- TP53WT, and four TP53LOF PSCC samples and six controls were included. Immunohistochemistry and hematoxylin-eosin confirmed the morphological context of the observed signatures. Prognostic differences between patient groups were validated in 541 PSCC patients using Kaplan-Meier survival estimates. KEY FINDINGS AND LIMITATIONS: Patients with aberrant p53 staining fare much worse than patients with either HPV- or HPV+ tumors and WT p53 expression. Using scRNA-seq, we revealed 65 cell subtypes within 83 682 cells. TP53LOF tumors exhibit a partial epithelial-to-mesenchymal transition, immune-excluded, angiogenic, and morphologically invasive environment, underlying their aggressive phenotype. HPV- TP53WT tumors show stemness and immune exhaustion. HPV+ TP53WT tumors mirror normal epithelial maturation with upregulation of antibody-drug-conjugate targets and activation of innate immunity. Inherent to the scRNA-seq analysis, low sample size is a limitation and validation of signatures in large PSCC cohorts is needed. CONCLUSIONS AND CLINICAL IMPLICATIONS: This first scRNA-seq atlas offers unprecedented in-depth insights into PSCC biology underlying prognostic differences based on TP53 and HPV status. Our findings provide clues for testing novel biomarker-driven therapies in PSCC. PATIENT SUMMARY: Here, we analyzed tissues of penile cancer at the level of individual cells, which helps us understand why patients who harbor a deactivating mutation in the TP53 gene do much worse than patients lacking such a mutation. Such an analysis may help us tailor future therapies based on TP53 gene mutations and human papillomavirus status of these tumors.

Oral Hygiene Status, Salivary and Microbiological Parameters Among Visually Impaired and Normal-Sighted Children After Specialized Oral Health Education: An Interventional Study.

Aim and objectives To assess oral hygiene status and salivary and microbiological parameters among 12 to 15-year-old visually impaired and normal-sighted children before and after oral health education (OHE). Methodology An interventional study was conducted among 25 visually impaired children (Group A) and 25 normal-sighted children (Group B) in the age range of 12 to 15 years. Simple random sampling was used to select the study participants. A questionnaire was designed to record socio-demographic data and the dietary habits of the children on pre-decided days. The oral hygiene practices and the Decayed Missing Filled Teeth (DMFT) index were recorded, and salivary physicochemical parameters for all the selected children were evaluated, followed by saliva collection for microbial analysis. After baseline assessment, the Audio-Tactile Performance technique for Group A and the animated visual performance technique for Group B children were used to impart OHE. Periodic assessments of salivary parameters were conducted at one-month and three-month intervals. Unpaired T test/Mann-Whitney U test, repeated measures analysis of variance (ANOVA)/Friedman test, followed by Bonferroni's post hoc test were carried out to determine the difference between and within groups, respectively. All statistical tests were performed at a significance level of 5%. Results Group A demonstrated a greater change in salivary pH (6.20 ± 0.41 to 6.96 ± 0.20), salivary buffering capacity (5.80 ± 0.82 to 7.20 ± 0.65), and Streptococcus mutans count (9.36 ± 0.41 to 8.7 ± 0.45 x 104 CFU/mL) when compared to Group B. Group B demonstrated a greater Lactobacillus acidophilus count reduction (7.96 ± 0.66 to 7.50 ± 0.64 x 104 CFU/mL) when compared to Group A. Conclusion The appropriate use of specialized OHE holds particular significance in the improvement of oral hygiene status and salivary parameters, along with a reduction in the bacterial count in both visually impaired children and normal-sighted children.

Multi-center study on mortality in children, and adults with sickle cell anemia-risk factors and causes of death.

Sickle cell disease (SCD) is a major public health burden worldwide with increasing morbidity and mortality. The study evaluates the risk factors associated with mortality in SCD patients, between the years 2006 and 2020 at three hospitals in Oman. The analysis includes clinical manifestations, haematological, biochemical, and radiological parameters, use of antibiotics, and blood and exchange transfusions. Our cohort included 123 patients (82 males, 41 females), with a median age of 27 (Interquartile Range 21-35 years). SCD related complications included acute chest syndrome (ACS) in 52.8%, splenic sequestration in 21.1%, right upper quadrant syndrome in 19.5%, more than > 6 VOC/year in 17.9%, and stroke in 13.8%. At the terminal admission, patients had cough, reduced O2 saturation, crepitation and fever in 24.4%, 49.6%, 53.6% and 68.3% respectively. Abnormal chest X-ray and chest CT scan were seen in 57.7%, and 76.4% respectively. Laboratory parameters showed a significant drop in hemoglobin (Hb) and platelet counts from baseline, with a significant rise in WBC, LDH and CRP from baseline (p < 0.05, Wilcoxon Signed Ranks test). All patients received antibiotics, whereas, 95.9% and 93.5% received simple blood transfusions, and exchange transfusions respectively, and 66.6% required non-invasive ventilation. Among the causes of death, ACS is seen in 32 (26%), sepsis in 49 (40%), and miscellaneous in 42 (34%). Sudden death was seen in 32 (26%) of patients. Male gender, with low HbF, rapid drop in Hb and platelet, and increased in WBC, LDH, ferritin, and CRP, correlated significantly with mortality in this cohort.

A visual-language foundation model for computational pathology.

The accelerated adoption of digital pathology and advances in deep learning have enabled the development of robust models for various pathology tasks across a diverse array of diseases and patient cohorts. However, model training is often difficult due to label scarcity in the medical domain, and a model's usage is limited by the specific task and disease for which it is trained. Additionally, most models in histopathology leverage only image data, a stark contrast to how humans teach each other and reason about histopathologic entities. We introduce CONtrastive learning from Captions for Histopathology (CONCH), a visual-language foundation model developed using diverse sources of histopathology images, biomedical text and, notably, over 1.17 million image-caption pairs through task-agnostic pretraining. Evaluated on a suite of 14 diverse benchmarks, CONCH can be transferred to a wide range of downstream tasks involving histopathology images and/or text, achieving state-of-the-art performance on histology image classification, segmentation, captioning, and text-to-image and image-to-text retrieval. CONCH represents a substantial leap over concurrent visual-language pretrained systems for histopathology, with the potential to directly facilitate a wide array of machine learning-based workflows requiring minimal or no further supervised fine-tuning.

Artificial intelligence algorithm accurately assesses oestrogen receptor immunohistochemistry in metastatic breast cancer cytology specimens: A pilot study.

OBJECTIVE: The Visiopharm artificial intelligence (AI) algorithm for oestrogen receptor (ER) immunohistochemistry (IHC) in whole slide images (WSIs) has been successfully validated in surgical pathology. This study aimed to assess its efficacy in cytology specimens. METHODS: The study cohort comprised 105 consecutive cytology specimens with metastatic breast carcinoma. ER IHC WSIs were seamlessly integrated into the Visiopharm platform from the Image Management System (IMS) during our routine digital workflow, and an AI algorithm was employed for analysis. ER AI scores were compared with pathologists' manual consensus scores. Optimization steps were implemented and evaluated to reduce discordance. RESULTS: The overall concordance between pathologists' scores and AI scores was excellent (99/105, 94.3%). Six cases exhibited discordant results, including two false-negative (FN) cases due to abundant histiocytes incorrectly counted as negatively stained tumour cells by AI, two FN cases owing to weak staining, and two false-positive (FP) cases where pigmented macrophages were erroneously counted as positively stained tumour cells by AI. The Pearson correlation coefficient of ER-positive percentages between pathologists' and AI scores was 0.8483. Optimization steps, such as lowering the cut-off threshold and additional training using higher input magnification, significantly improved accuracy. CONCLUSIONS: The automated ER AI algorithm demonstrated excellent concordance with pathologists' assessments and accurately differentiated ER-positive from ER-negative metastatic breast carcinoma cytology cases. However, precision in identifying tumour cells in cytology specimens requires further enhancement.

Artificial intelligence's impact on breast cancer pathology: a literature review.

This review discusses the profound impact of artificial intelligence (AI) on breast cancer (BC) diagnosis and management within the field of pathology. It examines the various applications of AI across diverse aspects of BC pathology, highlighting key findings from multiple studies. Integrating AI into routine pathology practice stands to improve diagnostic accuracy, thereby contributing to reducing avoidable errors. Additionally, AI has excelled in identifying invasive breast tumors and lymph node metastasis through its capacity to process large whole-slide images adeptly. Adaptive sampling techniques and powerful convolutional neural networks mark these achievements. The evaluation of hormonal status, which is imperative for BC treatment choices, has also been enhanced by AI quantitative analysis, aiding interobserver concordance and reliability. Breast cancer grading and mitotic count evaluation also benefit from AI intervention. AI-based frameworks effectively classify breast carcinomas, even for moderately graded cases that traditional methods struggle with. Moreover, AI-assisted mitotic figures quantification surpasses manual counting in precision and sensitivity, fostering improved prognosis. The assessment of tumor-infiltrating lymphocytes in triple-negative breast cancer using AI yields insights into patient survival prognosis. Furthermore, AI-powered predictions of neoadjuvant chemotherapy response demonstrate potential for streamlining treatment strategies. Addressing limitations, such as preanalytical variables, annotation demands, and differentiation challenges, is pivotal for realizing AI's full potential in BC pathology. Despite the existing hurdles, AI's multifaceted contributions to BC pathology hold great promise, providing enhanced accuracy, efficiency, and standardization. Continued research and innovation are crucial for overcoming obstacles and fully harnessing AI's transformative capabilities in breast cancer diagnosis and assessment.

Switching the on-surface orientation of oxygen-functionalized helicene.

Helicenes represent an important class of chiral organic material with promising optoelectronic properties. Hence, functionalization of surfaces with helicenes is a key step toward new organic materials devices. The deposition of a heterohelicene containing two furano groups and two hydroxyl groups onto copper(111) surface in ultrahigh vacuum leads to different adsorbate modifications. At low coverage and low temperature, the molecules tend to lie on the surface in order to maximize van der Waals contact with the substrate. Thermal treatment leads to deprotonation of the hydroxyl groups and in part into a reorientation from lying into a standing adsorbate mode.

Absence of 3a0 charge density wave order in the infinite-layer nickelate NdNiO2.

A hallmark of many unconventional superconductors is the presence of many-body interactions that give rise to broken-symmetry states intertwined with superconductivity. Recent resonant soft X-ray scattering experiments report commensurate 3a0 charge density wave order in infinite-layer nickelates, which has important implications regarding the universal interplay between charge order and superconductivity in both cuprates and nickelates. Here we present X-ray scattering and spectroscopy measurements on a series of NdNiO2+x samples, which reveal that the signatures of charge density wave order are absent in fully reduced, single-phase NdNiO2. The 3a0 superlattice peak instead originates from a partially reduced impurity phase where excess apical oxygens form ordered rows with three-unit-cell periodicity. The absence of any observable charge density wave order in NdNiO2 highlights a crucial difference between the phase diagrams of cuprate and nickelate superconductors.

Artificial Intelligence-Enabled Prostate Cancer Diagnosis and Prognosis: Current State and Future Implications.

In this modern era of digital pathology, artificial intelligence (AI)-based diagnostics for prostate cancer has become a hot topic. Multiple retrospective studies have demonstrated the benefits of AI-based diagnostic solutions for prostate cancer that includes improved prostate cancer detection, quantification, grading, interobserver concordance, cost and time savings, and a potential to reduce pathologists' workload and enhance pathology laboratory workflow. One of the major milestones is the Food and Drug Administration approval of Paige prostate AI for a second review of prostate cancer diagnosed using core needle biopsies. However, implementation of these AI tools for routine prostate cancer diagnostics is still lacking. Some of the limiting factors include costly digital pathology workflow, lack of regulatory guidelines for deployment of AI, and lack of prospective studies demonstrating the actual benefits of AI algorithms. Apart from diagnosis, AI algorithms have the potential to uncover novel insights into understanding the biology of prostate cancer and enable better risk stratification, and prognostication. This article includes an in-depth review of the current state of AI for prostate cancer diagnosis and highlights the future prospects of AI in prostate pathology for improved patient care.