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.

Factors associated with prolonged viral shedding of SARS-CoV-2 Omicron variant infection in Shanghai: A multicenter, retrospective, observational study.

Shanghai has faced an unprecedented COVID-19 pandemic with the BA.2.2 strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron infection. Comprehensive insights into its epidemiology, clinical manifestations, and viral shedding dynamics are currently limited. This study encompasses 208373 COVID-19 patients that were infected with the Omicron BA.2.2 sub-lineage in Shanghai, China. Demographic information, clinical symptoms, vaccination status, isolation status, as well as viral shedding time (VST) were recorded. Among the COVID-19 patients included in this study, 187124 were asymptomatic and 21249 exhibited mild symptoms. The median VST was 8.3 days. The common clinical symptoms included fever, persistent cough, phlegm, sore throat, and gastrointestinal symptoms. Factors such as advanced age, presence of comorbidities, mild symptomatology, and delayed isolation correlated with extended VST. Conversely, female gender and administration of two or three vaccine doses correlated with a reduction in VST. This investigation offers an in-depth characterization and analytical perspective on Shanghai's recent COVID-19 surge. Prolonged viral shedding of SARS-CoV-2 was observed in elderly, male, symptomatic patients, and those with comorbidity. Female, individuals with two or three vaccine doses, as well as those isolated early, shows an effective reduced VST.

Accurate detection of arrhythmias on raw electrocardiogram images: An aggregation attention multi-label model for diagnostic assistance.

BACKGROUND: The low rate of detection of abnormalities has been a major problem with current artificial intelligence-based electrocardiogram diagnostic algorithms, particularly when applied under real-world clinical scenarios. METHODS: We proposed an aggregation attention multilabel electrocardiogram classification model (AA-ECG) that can be applied directly to raw images to identify cardiac abnormalities using image-level annotation only. To develop and validate the model, we conducted a prospective two-site study to build two large-scale real-world datasets of 12-lead electrocardiogram images, annotated by clinical experts in a multilabeled manner. We compared the proposed model with seven state-of-the-art classifiers on both datasets in 27 main categories. RESULTS: In total, 47,733 electrocardiogram images from 37,442 consecutive patients were included in the development set, while 18,581 from 18,345 in the external set. The proposed model achieved better overall performance than the other seven models.The visualization of the attention maps provided an approach to build medical interpretability for machine intelligence. CONCLUSIONS: The proposed model had high diagnostic accuracy in identifying cardiac abnormalities on two real-world datasets. It has the potential to help clinicians provide more efficient cardiac care with fewer medical resources.

Risk factor analysis of omicron patients with mental health problems in the Fangcang shelter hospital based on psychiatric drug intervention during the COVID-19 pandemic in Shanghai, China.

Backgrounds: The widespread coronavirus disease 2019 (COVID-19) outbreak impacted the mental health of infected patients admitted to Fangcang shelter hospital a large-scale, temporary structure converted from existing public venues to isolate patients with mild or moderate symptoms of COVID-19 infection. Objective: This study aimed to investigate the risk factors of the infected patients from a new pharmacological perspective based on psychiatric drug consumption rather than questionnaires for the first time. Methods: We summarised the medical information and analysed the prevalence proportion, characteristics, and the related risk factors of omicron variants infected patients in the Fangcang Shelter Hospital of the National Exhibition and Convention Center (Shanghai) from 9 April 2022 to 31 May 2022. Results: In this study, 6,218 individuals at 3.57% of all admitted patients in the Fangcang shelter were collected suffering from mental health problems in severe conditions including schizophrenia, depression, insomnia, and anxiety who needed psychiatric drug intervention. In the group, 97.44% experienced their first prescription of psychiatric drugs and had no diagnosed historical psychiatric diseases. Further analysis indicated that female sex, no vaccination, older age, longer hospitalization time, and more comorbidities were independent risk factors for the drug-intervened patients. Conclusion: This is the first study to analyse the mental health problems of omicron variants infected patients hospitalised in Fangcang shelter hospitals. The research demonstrated the necessity of potential mental and psychological service development in Fangcang shelters during the COVID-19 pandemic and other public emergency responses.

Comorbidities prolonged viral shedding of patients infected with SARS-CoV-2 omicron variant in Shanghai: A multi-center, retrospective, observational study.

BACKGROUND: As the omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surges amid the coronavirus disease 2019 (COVID-19) pandemic, there is limited comorbidities data associated with viral shedding time (VST). We aimed to investigate the effect of comorbidities on VST in asymptomatic and mild patients with omicron. METHODS: A multi-center, retrospective, observational study was conducted from March 12, 2022 to May 24, 2022 in Shanghai. The analysis was adjusted for patients' baseline demographic, using log-rank test and logistic regression model. RESULTS: The study enrolled 198,262 subjects. The median duration of viral shedding time (VST) was 8.29 days. The number of cumulative viral shedding events was significantly lower in the chronic obstructive pulmonary disease (COPD), hyperlipidemia, diabetes, urinary system disease, and cardiocerebrovascular disease than in the no corresponding comorbidities group. Patients with comorbidities had a lower incidence of viral shedding, and the most significant independent risk factor is COPD (aOR 1.78, 95% CI: 1.53-2.08, p < 0.001). Across different age ranges, the comorbidities affecting viral shedding also differ, with the greatest risk factors for viral shedding being hyperlipidemia (aOR 2.23, 95% CI: 1.50-3.31, p < 0.001) and COPD (aOR 1.85, 95% CI: 1.50-2.28, p < 0.001) between ages of 18-39 and 40-64, and thyroid dysfunction (aOR 2.36, 95% CI: 1.60-3.47, p < 0.001) above age 64. CONCLUSIONS: Omicron-infected patients with comorbidities might prolong the VST. The independent risk factors also differ across age ranges, suggesting that providing targeted effective prevention and control guidance and allocating appropriate resources to different populations should be a crucial strategy.

Using Multi-Task Learning-Based Framework to Detect ST-Segment and J-Point Deviation From Holter.

Artificial intelligence is increasingly being used on the clinical electrocardiogram workflows. Few electrocardiograms based on artificial intelligence algorithms have focused on detecting myocardial ischemia using long-term electrocardiogram data. A main reason for this is that interference signals generated from daily activities while wearing the Holter monitor lowered the ability of artificial intelligence to detect myocardial ischemia. In this study, an automatic system combining denoising and segmentation modules was developed to detect the deviation of the ST-segment and J point. We proposed a ECG Bidirectional Transformer network that applied in both denoising and segmentation tasks. The denoising model achieved RMSEde, SNRimp, and PRD values of 0.074, 10.006, and 16.327, respectively. The segmentation model achieved precision, sensitivity (recall), and F1-score of 96.00, 93.06, and 94.51%, respectively. The system's ability to distinguish the depression and elevation of the ST-segment and J point was also verified by cardiologists as well. From our ECG dataset, 103 patients with ST-segment depression and 10 patients with ST-segment elevation were detected with positive predictive values of 80.6 and 60% respectively. Using Holter ECG and transformer-based deep neural networks, we can detect subtle ST-segment changes in noisy ECG signals. This system has the potential to improve the efficacy of daily medicine and to provide a broader population-level screening for asymptomatic myocardial ischemia.

Practical fine-grained learning based anomaly classification for ECG image.

As a widely used vital sign within cardiology, Electrocardiography (ECG) provides the basis for assessing heart function and diagnosing cardiovascular diseases. Automated anomaly detection for ECG plays an important role in improving patient diagnosis efficiency and reducing healthcare costs. Practically, due to the limits of electronics support or the medical system setting, image is a more common format for large-scale ECG storage in most clinical institutions. To guarantee an automated ECG detection model's scalability and practicality in clinical applications, taking good advantage of ECG images is crucial. However, existing time digital-based discriminative models fail to learn from images effectively for two reasons. First of all, the signals recorded on images have much lower resolution and higher noise, which makes it impractical to extract precise ECG signals following existing techniques. Meanwhile, the differences between abnormal signals are usually subtle, and they may be overwhelmed by the noises in the images as well. Towards this end, we design a novel neural framework that can be directly applied to massive ECG images determining various types of cardiology abnormalities. It classifies fine-grained ECG images based on weakly supervised strategy, in which case only image-level labeling is required. By eliminating the need for part annotations, the proposed method can result in significant savings in annotation time and cost. The effectiveness of the method is demonstrated by experimental results on two real ECG datasets.

Developments and Opportunities for 3D Bioprinted Organoids.

Organoids developed from pluripotent stem cells or adult stem cells are three-dimensional cell cultures possessing certain key characteristics of their organ counterparts, and they can mimic certain biological developmental processes of organs in vitro. Therefore, they have promising applications in drug screening, disease modeling, and regenerative repair of tissues and organs. However, the construction of organoids currently faces numerous challenges, such as breakthroughs in scale size, vascularization, better reproducibility, and precise architecture in time and space. Recently, the application of bioprinting has accelerated the process of organoid construction. In this review, we present current bioprinting techniques and the application of bioinks and summarize examples of successful organoid bioprinting. In the future, a multidisciplinary combination of developmental biology, disease pathology, cell biology, and materials science will aid in overcoming the obstacles pertaining to the bioprinting of organoids. The combination of bioprinting and organoids with a focus on structure and function can facilitate further development of real organs.

Brønsted Acid-Catalyzed Tandem Cyclizations of Tryptamine-Ynamides Yielding 1H-Pyrrolo[2,3-d]carbazole Derivatives.

Ynamides, as versatile synthetic precursors, have attracted much attention from synthetic chemists and sparked the development of a number of methodologies for the construction of various structures. 1H-Pyrrolo[2,3-d]carbazole is a core scaffold of a series of monoterpene indole alkaloids found in Kopsia, Strychnos, and Aspidosperma, for example. In this study, 1H-pyrrolo[2,3-d]carbazole derivatives were synthesized by a Brønsted acid-catalyzed tandem cyclization starting from tryptamine-based ynamides. This strategy prevented Wagner-Meerwein rearrangement by instantaneous intramolecular nucleophilic trapping of the indoleninium to afford a tetracyclic indoline via an in situ-formed enol species induced by the formation of a more stable conjugate diene moiety. The functional group tolerances were investigated by using a series of readily available substrates. A plausible mechanism has been proposed based on the evidence of the capture of the hemiaminal intermediate. Lastly, a Büchi ketone, which is the pivotal intermediate in the synthesis of the indole alkaloid vindorosine, was synthesized by utilizing our newly developed methodology.

Collective Syntheses of 2-(3-Methylbenzofuran-2-yl)phenol-Derived Natural Products by a Cascade [3,3]-Sigmatropic Rearrangement/Aromatization Strategy.

A cascade [3,3]-sigmatropic rearrangement/aromatization strategy to the synthesis of 2-(3-methylbenzofuran-2-yl)phenol derivatives was developed and applied to the collective syntheses of seven 2-arylbenzofuran-containing natural products, namely glycybenzofuran, glycyuralin E, lespedezol A1, puerariafuran, 7,2',4'-trihydroxy-3-benzofurancarboxylic acid, coumestrol, and 4'-O-methylcoumestrol. Among them, the total syntheses of glycybenzofuran, glycyuralin E, puerariafuran, 7,2',4'-trihydroxy-3-benzofurancarboxylic acid, and 4'-O-methylcoumestrol were reported for the first time. The practicality of this novel strategy in preparation of the key intermediates was demonstrated by performing the reaction on gram scale and by synthesizing a series of natural products with 2-(3-methylbenzofuran-2-yl)phenol scaffolds in a common strategy.

Facile Synthesis of Phosphate-Functionalized MWCNT-TiO2 Nanocomposites as Efficient Photocatalysts and Insights into the Roles of Nanostructured Carbon.

Effectively contacted multiwalled carbon nanotube (MWCNT)-titanium dioxide nanocomposites and phosphate-functionalized MWCNT-TiO2 composites have been successfully synthesized by simple one-pot phase-separated hydrolysis-solvothermal processes. The key to this synthetic strategy is to disperse MWCNTs uniformly in Ti(OBu)4 in advance and then to put them into the toluene organic phase. The as-prepared nanocomposites between TiO2 and the correct amount of MWCNT exhibits higher activity in the photocatalytic degradation of rhodamine B than that with the resulting TiO2 , although the activity in the photocatalytic degradation of gas-phase aldehyde and liquid-phase phenol is lower. Interestingly, the functionalization of MWCNTs with an appropriate amount of phosphoric acids prior to the synthesis could greatly improve the activity of the MWCNT-TiO2 nanocomposites for the degradation of aldehyde and phenol, even superior to that of commercial P25 TiO2 . Based on the measurements of atmosphere-controlled surface photovoltage spectra and O2 temperature-programmed desorption, it is suggested that MWCNTs are favorable to increase rhodamine B adsorption on the composite to promote the photosensitization oxidation reactions, whereas it is unfavorable for the adsorption of O2 and responsible for the low photocatalytic activity for the degradation of colorless pollutants. Phosphate functionalization greatly enhances the amount of O2 adsorbed on the MWCNT, which leads to significant charge separation, and thus, to significant photoactivity for the degradation of colored and colorless pollutants of the nanocomposites.

Synthesis of nanocrystalline anatase TiO2 by one-pot two-phase separated hydrolysis-solvothermal processes and its high activity for photocatalytic degradation of rhodamine B.

Si-doped and un-doped nanocrystalline TiO(2) samples have been synthesized by simple one-pot water-organic two-phase separated hydrolysis-solvothermal (HST) processes, and characterized by XRD, BET, TEM, FT-IR, DRS and surface photovoltage techniques. The effects of the solvothermal temperature and Si doping on the anatase thermal stability, and on the photocatalytic activity for degrading rhodamine B were investigated in detail. The results show that, as the solvothermal temperature rises, the crystallinity and thermal stability of the resulting nano-sized anatase TiO(2) gradually increase. Noticeably, the as-prepared TiO(2) obtained at appropriate solvothermal temperature (160 degrees C) exhibits high photocatalytic activity. Moreover, although Si doping does not improve the photocatalytic activity of the as-prepared anatase TiO(2), it greatly enhances the anatase thermal stability and inhibits crystallite growth during the process of post-thermal treatment. Interestingly, the Si-doped TiO(2) post-treated at high temperature displays much higher photocatalytic activity than commercial P25 TiO(2). It is clearly demonstrated that the joint effects of high anatase crystallinity and large surface area lead to high photocatalytic activity. This work provides a simple and effective strategy for the synthesis of high-performance TiO(2)-based functional nanomaterials.