Genotype, cervical intraepithelial neoplasia, and type-specific cervical intraepithelial neoplasia distributions in hrHPV+ cases referred to colposcopy: A multicenter study of Chinese mainland women.

To investigate age and type-specific prevalences of high-risk human papillomavirus (hrHPV) and cervical intraepithelial neoplasia (CIN) in hrHPV+ women referred to colposcopy. This is a retrospective, multicenter study. Participants were women referred to one of seven colposcopy clinics in China after testing positive for hrHPV. Patient characteristics, hrHPV genotyping, colposcopic impressions, and histological diagnoses were abstracted from electronic records. Main outcomes were age-related type-specific prevalences associated with hrHPV and CIN, and colposcopic accuracy. Among 4419 hrHPV+ women referred to colposcopy, HPV 16, 52, and 58 were the most common genotypes. HPV 16 prevalence was 39.96%, decreasing from 42.57% in the youngest group to 30.81% in the eldest group. CIN3+ prevalence was 15.00% and increased with age. As lesion severity increases, HPV16 prevalence increased while the prevalence of HPV 52 and 58 decreased. No age-based trend was identified with HPV16 prevalence among CIN2+, and HPV16-related CIN2+ was less common in women aged 60 and above (44.26%) compared to those younger than 60 years (59.61%). Colposcopy was 0.73 sensitive at detecting CIN2+ (95% confidence interval[CI]: 0.71, 0.75), with higher sensitivity (0.77) observed in HPV16+ women (95% CI: 0.74, 0.80) compared to HPV16- women (0.68, 95% CI: 0.64, 0.71). Distributions of hrHPV genotypes, CIN, and type-specific CIN in Chinese mainland hrHPV+ women referred to colposcopy were investigated for the first time. Distributions were found to be age-dependent and colposcopic performance appears related to HPV genotypes. These findings could be used to improve the management of women referred to colposcopy.

Distribution and diagnostic value of single and multiple high-risk HPV infections in detection of cervical intraepithelial neoplasia: A retrospective multicenter study in China.

The risk associated with single and multiple human papillomavirus (HPV) infections in cervical intraepithelial neoplasia (CIN) remains uncertain. This study aims to explore the distribution and diagnostic significance of the number of high-risk HPV (hr-HPV) infections in detecting CIN, addressing a crucial gap in our understanding. This comprehensive multicenter, retrospective study meticulously analyzed the distribution of single and multiple hr-HPV, the risk of CIN2+, the relationship with CIN, and the impact on the diagnostic performance of colposcopy using demographic information, clinical histories, and tissue samples. The composition of a single infection was predominantly HPV16, 52, 58, 18, and 51, while HPV16 and 33 were identified as the primary causes of CIN2+. The primary instances of dual infection were mainly observed in combinations such as HPV16/18, HPV16/52, and HPV16/58, while HPV16/33 was identified as the primary cause of CIN2+. The incidence of hr-HPV infections shows a dose-response relationship with the risk of CIN (p for trend <0.001). Compared to single hr-HPV, multiple hr-HPV infections were associated with increased risks of CIN1 (1.44, 95% confidence interval [CI]: 1.20-1.72), CIN2 (1.70, 95% CI: 1.38-2.09), and CIN3 (1.08, 95% CI: 0.86-1.37). The colposcopy-based specificity of single hr-HPV (93.4, 95% CI: 92.4-94.4) and multiple hr-HPV (92.9, 95% CI: 90.8-94.6) was significantly lower than negative (97.9, 95% CI: 97.0-98.5) in detecting high-grade squamous intraepithelial lesion or worse (HSIL+). However, the sensitivity of single hr-HPV (73.5, 95% CI: 70.8-76.0) and multiple hr-HPV (71.8, 95% CI: 67.0-76.2) was higher than negative (62.0, 95% CI: 51.0-71.9) in detecting HSIL+. We found that multiple hr-HPV infections increase the risk of developing CIN lesions compared to a single infection. Colposcopy for HSIL+ detection showed high sensitivity and low specificity for hr-HPV infection. Apart from HPV16, this study also found that HPV33 is a major pathogenic genotype.

A continuously efficient O2-supplying strategy for long-term modulation of hypoxic tumor microenvironment to enhance long-acting radionuclides internal therapy.

Radionuclides internal radiotherapy (RIT) is a clinically powerful method for cancer treatment, but still poses unsatisfactory therapeutic outcomes due to the hypoxic characteristic of tumor microenvironment (TME). Catalase (CAT) or CAT-like nanomaterials can be used to enzymatically decompose TME endogenous H2O2 to boost TME oxygenation and thus alleviate the hypoxic level within tumors, but their effectiveness is still hindered by the short-lasting of hypoxia relief owing to their poor stability or degradability, thereby failing to match the long therapeutic duration of RIT. Herein, we proposed an innovative strategy of using facet-dependent CAT-like Pd-based two-dimensional (2D) nanoplatforms to continuously enhance RIT. Specifically, rationally designed 2D Pd@Au nanosheets (NSs) enable consistent enzymatic conversion of endogenous H2O2 into O2 to overcome hypoxia-induced RIT resistance. Furthermore, partially coated Au layer afford NIR-II responsiveness and moderate photothermal treatment that augmenting their enzymatic functionality. This approach with dual-effect paves the way for reshaping TME and consequently facilitating the brachytherapy ablation of cancer. Our work offers a significant advancement in the integration of catalytic nanomedicine and nuclear medicine, with the overarching goal of amplifying the clinical benefits of RIT-treated patients.

An assessment of ChatGPT's responses to frequently asked questions about cervical and breast cancer.

BACKGROUND: Cervical cancer (CC) and breast cancer (BC) threaten women's well-being, influenced by health-related stigma and a lack of reliable information, which can cause late diagnosis and early death. ChatGPT is likely to become a key source of health information, although quality concerns could also influence health-seeking behaviours. METHODS: This cross-sectional online survey compared ChatGPT's responses to five physicians specializing in mammography and five specializing in gynaecology. Twenty frequently asked questions about CC and BC were asked on 26th and 29th of April, 2023. A panel of seven experts assessed the accuracy, consistency, and relevance of ChatGPT's responses using a 7-point Likert scale. Responses were analyzed for readability, reliability, and efficiency. ChatGPT's responses were synthesized, and findings are presented as a radar chart. RESULTS: ChatGPT had an accuracy score of 7.0 (range: 6.6-7.0) for CC and BC questions, surpassing the highest-scoring physicians (P < 0.05). ChatGPT took an average of 13.6 s (range: 7.6-24.0) to answer each of the 20 questions presented. Readability was comparable to that of experts and physicians involved, but ChatGPT generated more extended responses compared to physicians. The consistency of repeated answers was 5.2 (range: 3.4-6.7). With different contexts combined, the overall ChatGPT relevance score was 6.5 (range: 4.8-7.0). Radar plot analysis indicated comparably good accuracy, efficiency, and to a certain extent, relevance. However, there were apparent inconsistencies, and the reliability and readability be considered inadequate. CONCLUSIONS: ChatGPT shows promise as an initial source of information for CC and BC. ChatGPT is also highly functional and appears to be superior to physicians, and aligns with expert consensus, although there is room for improvement in readability, reliability, and consistency. Future efforts should focus on developing advanced ChatGPT models explicitly designed to improve medical practice and for those with concerns about symptoms.

Enhancing colposcopy training using a widely accessible digital education tool in China.

BACKGROUND: Colposcopy is a cornerstone of cervical cancer prevention; however, there is a global shortage of colposcopists. It is challenging to train a sufficient number of colposcopists through in-person methods, which hinders our ability to adequately diagnose and manage positive cases. A digital platform is needed to make colposcopy training more efficient, scalable, and sustainable; however, current online training programs are generally based on didactic curricula that do not incorporate image analysis training. In addition, long-term assessments of online training are not readily available. Therefore, innovative digital training and an assessment of its effectiveness are needed. OBJECTIVE: This study aimed to evaluate the short- and long-term effects of DECO (an online Digital Education Tool for Colposcopy) on trainees' colposcopy competencies and confidence. STUDY DESIGN: DECO can be used both on laptops and smartphones and comprises 4 training modules (image interpretation; terminology learning; video teaching; and collection of guidelines and typical cases) and 2 test modules. DECO was tested through a pre-post study between September and November 2022. Participants were recruited in China, and DECO training lasted 12 days. Trainees initially learned basic theory before completing training using 200 image-based cases. Pretest, posttest, and follow-up testing included 20 distinct image-based questions, and was conducted on Days 0, 13, and 60. Primary outcomes were competence and confidence scores. Secondary measures were response distributions for colposcopic diagnoses, biopsies, and DECO training satisfaction. Multilevel modeling was used to determine improvement from baseline to posttraining and follow-up for the outcomes of interest. RESULTS: Among 402 participants recruited, 96.8% (n=389) completed pretesting, 84.1% (n=338) posttesting, and 75.1% (n=302) follow-up testing. Colposcopic competence and confidence increased across this study. Diagnostic scores improved on average from 55.3 (53.7-56.9) to 70.4 (68.9-71.9). The diagnostic accuracy for normal/benign lesions, low-grade squamous intraepithelial lesions, and high-grade squamous intraepithelial lesions or worse increased by 16.9%, 13.1%, and 16.9%, respectively. Mean confidence scores increased from 48.1 (45.6-50.6) to 56.2 (54.5-57.9). These improvements remained evident 2 months after training. Trainees were also satisfied with DECO overall. Most found DECO to be scientific (82.5%), easy to use (75.2%), and clinically useful (98.4%), and would recommend it to colleagues (93.2%). CONCLUSION: DECO is a useful, acceptable digital education tool that improves colposcopy competencies and confidence. DECO could make colposcopy training more efficient, scalable, and sustainable because there are no geographic or time limitations. Therefore, DECO could be used to alleviate the shortage of trained colposcopists around the world.

Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

BACKGROUND: A number of publications have demonstrated that deep learning (DL) algorithms matched or outperformed clinicians in image-based cancer diagnostics, but these algorithms are frequently considered as opponents rather than partners. Despite the clinicians-in-the-loop DL approach having great potential, no study has systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. OBJECTIVE: We systematically quantified the diagnostic accuracy of clinicians with and without the assistance of DL in image-based cancer identification. METHODS: PubMed, Embase, IEEEXplore, and the Cochrane Library were searched for studies published between January 1, 2012, and December 7, 2021. Any type of study design was permitted that focused on comparing unassisted clinicians and DL-assisted clinicians in cancer identification using medical imaging. Studies using medical waveform-data graphics material and those investigating image segmentation rather than classification were excluded. Studies providing binary diagnostic accuracy data and contingency tables were included for further meta-analysis. Two subgroups were defined and analyzed, including cancer type and imaging modality. RESULTS: In total, 9796 studies were identified, of which 48 were deemed eligible for systematic review. Twenty-five of these studies made comparisons between unassisted clinicians and DL-assisted clinicians and provided sufficient data for statistical synthesis. We found a pooled sensitivity of 83% (95% CI 80%-86%) for unassisted clinicians and 88% (95% CI 86%-90%) for DL-assisted clinicians. Pooled specificity was 86% (95% CI 83%-88%) for unassisted clinicians and 88% (95% CI 85%-90%) for DL-assisted clinicians. The pooled sensitivity and specificity values for DL-assisted clinicians were higher than for unassisted clinicians, at ratios of 1.07 (95% CI 1.05-1.09) and 1.03 (95% CI 1.02-1.05), respectively. Similar diagnostic performance by DL-assisted clinicians was also observed across the predefined subgroups. CONCLUSIONS: The diagnostic performance of DL-assisted clinicians appears better than unassisted clinicians in image-based cancer identification. However, caution should be exercised, because the evidence provided in the reviewed studies does not cover all the minutiae involved in real-world clinical practice. Combining qualitative insights from clinical practice with data-science approaches may improve DL-assisted practice, although further research is required. TRIAL REGISTRATION: PROSPERO CRD42021281372; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=281372.

Correction: Unassisted Clinicians Versus Deep Learning-Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis.

[This corrects the article DOI: 10.2196/43832.].

QKI regulates adipose tissue metabolism by acting as a brake on thermogenesis and promoting obesity.

Adipose tissue controls numerous physiological processes, and its dysfunction has a causative role in the development of systemic metabolic disorders. The role of posttranscriptional regulation in adipose metabolism has yet to be fully understood. Here, we show that the RNA-binding protein quaking (QKI) plays an important role in controlling metabolic homeostasis of the adipose tissue. QKI-deficient mice are resistant to high-fat-diet (HFD)-induced obesity. Additionally, QKI depletion increased brown fat energy dissipation and browning of subcutaneous white fat. Adipose tissue-specific depletion of QKI in mice enhances cold-induced thermogenesis, thereby preventing hypothermia in response to cold stimulus. Further mechanistic analysis reveals that QKI is transcriptionally induced by the cAMP-cAMP response element-binding protein (CREB) axis and restricts adipose tissue energy consumption by decreasing stability, nuclear export, and translation of mRNAs encoding UCP1 and PGC1α. These findings extend our knowledge of the significance of posttranscriptional regulation in adipose metabolic homeostasis and provide a potential therapeutic target to defend against obesity and its related metabolic diseases.

Development and validation of a predictive model for endocervical curettage in patients referred for colposcopy: A multicenter retrospective diagnostic study in China.

Objective: This study aimed to develop a nomogram that can predict occult high-grade squamous intraepithelial lesions or worse (HSIL+) and determine the need for endocervical curettage (ECC) in patients referred for colposcopy. Methods: This retrospective multicenter study included 4,149 patients who were referred to any one of six tertiary hospitals in China for colposcopy between January 2020 and November 2021 because of abnormal screening results. ECC data were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to identify factors that could predict HSIL+ on ECC. Patients were randomly assigned to a training set or to an internal validation set for performance and comparability testing. The model was externally validated and tested in patients from two additional hospitals. The nomogram was assessed in terms of discrimination and calibration and subjected to decision curve analysis. Results: HSIL+ was found on ECC in 38.8% (n=388) of cases. Our predictive nomogram included age group, cytology, human papillomavirus (HPV) status, visibility of the cervix and colposcopic impression. The nomogram had good overall discrimination, which was internally validated [area under the receiver-operator characteristic (AUC), 0.839; 95% confidence interval (95% CI), 0.773-0.904]. In terms of external validation, the AUC was 0.843 (95% CI, 0.773-0.912) for the consecutive sample and 0.843 (95% CI, 0.783-0.902) for the comparative sample. Calibration analysis suggested good consistency between predicted and observed probabilities. Decision curve analysis suggested this nomogram would be clinically useful with almost the entire range of threshold probabilities. Conclusions: This internally and externally validated nomogram can be easily applied and incorporates multiple clinically relevant variables that can be used to identify patients with occult HSIL+ who need ECC.

A Novel Cascade Nanoreactor Integrating Two-Dimensional Pd-Ru Nanozyme, Uricase and Red Blood Cell Membrane for Highly Efficient Hyperuricemia Treatment.

Nanozyme-based cascade reaction has emerged as an effective strategy for disease treatment because of its high efficiency and low side effects. Herein, a new and highly active two-dimensional Pd-Ru nanozyme is prepared and then integrated with uricase and red blood cell (RBC) membrane to fabricate a tandem nanoreactor, Pd-Ru/Uricase@RBC, for hyperuricemia treatment. The designed Pd-Ru/Uricase@RBC nanoreactor displayed not only good stability against extreme pH, temperature and proteolytic degradation, but also long circulation half-life and excellent safety. The nanoreactor can effectively degrade UA by uricase to allantoin and H2 O2 and remove H2 O2 by using Pd-Ru nanosheets (NSs) with the catalase (CAT)-like activity. More importantly, the finally produced O2 from H2 O2 decomposition can in turn facilitate the catalytic oxidation of UA, as the degradation of UA is an O2 consumption process. By integrating the high-efficiency enzymatic activity, long circulation capability, and good biocompatibility, the designed Pd-Ru/Uricase@RBC can effectively and safely treat hyperuricemia without side effects. The study affords a new alternative for the exploration of clinical treatment of hyperuricemia.

Earlier changes in mice after D-galactose treatment were improved by mitochondria derived small peptide MOTS-c.

MOTS-c, as a mitochondria derived peptide, exerts benefits for insulin resistance in HFD mice and against various stresses in an AMPK dependent way. Here, in the D-galactose chronic injection models, exogenous MOTS-c was given to determine its direct anti-aging effects. The body weight, insulin sensitivity and blood glucose were determined with mild differences. Tissue morphology analyses disclosed that liver, visceral fat and dermal skin, all displayed aberrant lipid depositions in the D-galactose mice. MOTS-c treatment largely alleviated the lipid accumulations, corresponding with positive changes in mitochondria dynamics, observed in liver transmission electron microscopy and in altered mRNA levels of Drp1 and mitofusins. Notably, the aging phenotypes of small intestine tract were more obvious, including histological defects and lower Ki67 levels, plus with the higher levels of DNA stress, such as P21 and P16, as well as mitochondria dynamics. Collectively, these data provided the direct evidence to support that exogenous givings of MOTS-c prevented abnormal fat accumulations in D-gal mice, putatively via improvement of mitochondria dynamic related pathways.

MOTS-c inhibits Osteolysis in the Mouse Calvaria by affecting osteocyte-osteoclast crosstalk and inhibiting inflammation.

The Mitochondrial-derived peptide MOTS-c has recently been reported as a 16-amino acid peptide regulating metabolism and homeostasis in different cells. However, its effects on immune cells and bone metabolism are rarely reported. Here we demonstrate that MOTS-c treatment in ultra-high molecular weight polyethylene (UHMWPE) particle-induced osteolysis mouse model alleviated bone erosion and inflammation. MOTS-c increased osteoprotegerin (OPG)/ receptor activator of nuclear factor kappa-B ligand (RANKL) ratio in osteocytes, leading to inhibition of osteoclastogenesis. In primary bone marrow macrophages (BMMs) MOTS-c alleviated STAT1 and NF-κB phosphorylation triggered by UHMWPE particles. Promoting ROS production or suppressing peroxisome proliferator-activated receptor γ (PPARγ) coactivator-1α (PGC-1α) by adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) repression blocked these anti-inflammatory effects of MOTS-c treatment. Taken together, these findings provide evidence that the small peptide inhibits osteoclastogenesis by regulating osteocyte OPG/RANKL secretion and suppressing inflammation via restraining NF-κB and STAT1 pathway. Moreover, its effects on NF-κB activation is dependent on the AMPK-PGC-1α-ROS axis, suggesting its potential use in osteolysis and other inflammation disorders.

Metformin protects bone mass in ultra-high-molecular-weight polyethylene particle-induced osteolysis by regulating osteocyte secretion.

Metformin, an anti-hyperglycemic agent used for type 2 diabetes, has recently been found to have more effects apart from glucose regulation. We found that, in ultra-high-molecular-weight polyethylene particle-induced osteolysis mouse models, metformin had bone protect property and reduced the negative regulator of bone formation sclerostin (SOST) and Dickkopf-related protein 1 (DKK1), and increased osteoprotegerin (OPG) secretion and the ratio of OPG/Receptor Activator for Nuclear Factor-κB Ligand (RANKL). In vitro, we established a 3D co-culture system in which metformin affects osteoblasts and osteoclasts through mature osteocytes secretion. Metformin (50 µM) significantly decreased SOST and DKK1 mRNA expression, stimulating alkaline phosphatase activity and proliferation of osteoblast, and increased OPG secretion and the ratio of OPG/RANKL, inhibiting osteoclastogenesis. Moreover, the effect on OPG was reversed by adenosine 5'-monophosphate-activated protein kinase inhibitor, Compound C. Our finding suggests that metformin induces differentiation and mineralization of osteoblasts, while inhibits osteoclastogenesis via mature osteocytes secretion. Therefore, the drug might be beneficial for not only diabetes but also in other bone disorders by acting on mature osteocytes.

RNA-binding protein QKI regulates contact inhibition via Yes-associate protein in ccRCC.

Contact inhibition adjusts organ size to the proper size and ensures the cultured cells growing to a monolayer. By regulating the downstream coordinator YAP, the evolutionarily conserved Hippo transduction pathway attunes cell growth and death in response to cell contact inhibition, polarity, self-renewal, and differentiation. Dysregulation of this pathway is involved in various diseases such as cancer. RNA-binding protein QKI regulates cell proliferation, metabolism, division, and immunity in various cancer models, but its role in cancer cell contact inhibition remains unclear. In this study, we aimed to clarify the relationship between QKI and YAP, and the role of their interaction in cell contact inhibition. We found a lower QKI expression level in sparse condition, whereas a higher expression level in confluent condition by western blot analysis and immunofluorescence assay. QKI knockdown elevated cell proliferation and invasion both in vitro and in vivo. Strikingly, the results of CCK-8 assay, colony formation assay, and transwell assay showed that the phenomenon was in accord with the expression level of pYAP and reverse with YAP. Higher levels of Wnt3a and ß-catenin were also found in xenografts of QKI-knockdown clear cell renal cell carcinoma (ccRCC) CAKI-1 cells by western blot analysis and immumohistochemical staining. Finally, a positive correlation between QKI and pYAP was found in clinical specimens by immunohistochemistry. Thus, as a negative regulator of YAP, QKI attuned the cell contact inhibition, leading to inhibition of cancer cell proliferation and invasion through Wnt and GPCR pathway.

Curcumin promotes browning of white adipose tissue in a norepinephrine-dependent way.

Brown adipose tissue converts energy from food into heat via the mitochondrial uncoupling protein UCP1, defending against cold. In some conditions, inducible 'brown-like' adipocytes, also known as beige adipocytes, can develop within white adipose tissue (WAT). These beige adipocytes have characteristics similar to classical brown adipocytes and thus can burn lipids to produce heat. In the current study, we demonstrated that curcumin (50 or 100 mg/kg/day) decreased bodyweight and fat mass without affecting food intake in mice. We further demonstrated that curcumin improves cold tolerance in mice. This effect was possibly mediated by the emergence of beige adipocytes and the increase of thermogenic gene expression and mitochondrial biogenesis in inguinal WAT. In addition, curcumin promotes ß3AR gene expression in inguinal WAT and elevates the levels of plasma norepinephrine, a hormone that can induce WAT browning. Taken together, our data suggest that curcumin can potentially prevent obesity by inducing browning of inguinal WAT via the norepinephrine-ß3AR pathway.

Novel Coumarins Derivatives for A. baumannii Lung Infection Developed by High-Throughput Screening and Reinforcement Learning.

With the increasing resistance of Acinetobacter baumannii (A. baumannii) to antibiotics, researchers have turned their attention to the development of new antimicrobial agents. Among them, coumarin-based heterocycles have attracted much attention due to their unique biological activities, especially in the field of antibacterial infection. In this study, a series of coumarin derivatives were synthesized and screened for their bactericidal activities (Ren et al. 2018; Salehian et al. 2021). The inhibitory activities of these compounds on bacterial strains were evaluated, and the related mechanism of the new compounds was explored. Firstly, the MIC values and bacterial growth curves were measured after compound treatment to evaluate the antibacterial activity in vitro. Then, the in vivo antibacterial activities of the new compounds were assessed on A. baumannii-infected mice by determining the mice survival rates, counting bacterial CFU numbers, measuring inflammatory cytokine levels, and histopathology analysis. In addition, the ROS levels in the bacterial cells were measured with DCFH-DA detection kit. Furthermore, the potential target and detailed mechanism of the new compounds during infection disease therapy were predicted and evidenced with molecular docking. After that, ADMET characteristic prediction was completed, and novel, synthesizable, drug-effective molecules were optimized with reinforcement learning study based on the probed compound as a training template. The interaction between the selected structures and target proteins was further evidenced with molecular docking. This series of innovative studies provides important theoretical and experimental data for the development of new anti-A. baumannii infection drugs.

Chinese colposcopists' attitudes toward the colposcopic artificial intelligence auxiliary diagnostic system (CAIADS): A nation-wide, multi-center survey.

Objective: The objective of this study was to assess the attitudes toward the Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) of colposcopists working in mainland China. Methods: A questionnaire was developed to collect participants' sociodemographic information and assess their awareness, attitudes, and acceptance toward the CAIADS. Results: There were 284 respondents from 24 provinces across mainland China, with 55% working in primary care institutions. Participant data were divided into two subgroups based on their colposcopy case load per year (i.e. ≥50 cases; <50 cases). The analysis showed that participants with higher loads had more experience working with CAIADS and were more knowledgeable about CAIADS and AI systems. Overall, in both groups, about half of the participants understood the potential applications of big data and AI-assisted diagnostic systems in medicine. Although less than one-third of the participants were knowledgeable about CAIADS and its latest developments, more than 90% of the participants were open with the idea of using CAIADS. Conclusions: While a related lack of acknowledgement of CAIADS exists, the participants in general had an open attitude toward CAIADS. Practical experience with colposcopy or CAIADS contributed to participants' awareness and positive attitudes. The promotion of AI tools like CAIADS could help address regional health inequities to improve women's well-being, especially in low- and middle-income countries.

Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis.

BACKGROUND: Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics. OBJECTIVES: To critically assess different DL performances in diagnosing melanoma using dermatoscopic images and discuss the relationship between dermatologists and DL. METHODS: Ovid-Medline, Embase, IEEE Xplore, and the Cochrane Library were systematically searched from inception until 7th December 2021. Studies that reported diagnostic DL model performances in detecting melanoma using dermatoscopic images were included if they had specific outcomes and histopathologic confirmation. Binary diagnostic accuracy data and contingency tables were extracted to analyze outcomes of interest, which included sensitivity (SEN), specificity (SPE), and area under the curve (AUC). Subgroup analyses were performed according to human-machine comparison and cooperation. The study was registered in PROSPERO, CRD42022367824. RESULTS: 2309 records were initially retrieved, of which 37 studies met our inclusion criteria, and 27 provided sufficient data for meta-analytical synthesis. The pooled SEN was 82 % (range 77-86), SPE was 87 % (range 84-90), with an AUC of 0.92 (range 0.89-0.94). Human-machine comparison had pooled AUCs of 0.87 (0.84-0.90) and 0.83 (0.79-0.86) for DL and dermatologists, respectively. Pooled AUCs were 0.90 (0.87-0.93), 0.80 (0.76-0.83), and 0.88 (0.85-0.91) for DL, and junior and senior dermatologists, respectively. Analyses of human-machine cooperation were 0.88 (0.85-0.91) for DL, 0.76 (0.72-0.79) for unassisted, and 0.87 (0.84-0.90) for DL-assisted dermatologists. CONCLUSIONS: Evidence suggests that DL algorithms are as accurate as senior dermatologists in melanoma diagnostics. Therefore, DL could be used to support dermatologists in diagnostic decision-making. Although, further high-quality, large-scale multicenter studies are required to address the specific challenges associated with medical AI-based diagnostics.

Profibrogenic macrophage-targeted delivery of mitochondrial protector via exosome formula for alleviating pulmonary fibrosis.

Pulmonary fibrosis (PF) is a devastating lung disease with limited treatment options. During this pathological process, the profibrogenic macrophage subpopulation plays a crucial role, making the characterization of this subpopulation fundamentally important. The present study revealed a positive correlation between pulmonary macrophages with higher mitochondrial mass (Mømitohigh) and fibrosis. Among the Mømitohigh subpopulation of CD206+ M2, characterized by higher expression of dynamin 1-like (Drp1), as determined by flow cytometry and RNA-seq analysis, a therapeutic intervention was developed using an exosome-based formula composed of pathfinder and therapeutics. A pathfinder exosome called "exosomeMMP19 (ExoMMP19)", was constructed to display matrix metalloproteinase-19 (MMP19) on the surface to locally break down the excessive extracellular matrix (ECM) in the fibrotic lung. A therapeutic exosome called "exosome therapeutics (ExoTx)", was engineered to display D-mannose on the surface while encapsulating siDrp1 inside. Prior delivery of ExoMMP19 degraded excessive ECM and thus paved the way for ExoTx to be delivered into Mømitohigh, where ExoTx inhibited mitochondrial fission and alleviated PF. This study has not only identified Mømitohigh as profibrotic macrophages but it has also provided a potent strategy to reverse PF via a combination of formulated exosomes.

Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.

BACKGROUND: Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. OBJECTIVE: In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. METHODS: PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. RESULTS: In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. CONCLUSIONS: Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. TRIAL REGISTRATION: PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.