Anxiety and depression in nasopharyngeal carcinoma patients and network analysis to identify central symptoms: A cross-sectional study from a high-incidence area.

PURPOSE: To determine the prevalence of anxiety and depression in patients with nasopharyngeal carcinoma (NPC) and to identify central symptoms and bridge symptoms among psychiatric disorders. METHODS: This cross-sectional study recruited patients with NPC in Guangzhou, China from May 2022, to October 2022. The General Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) were used for screening anxiety and depression, respectively. Network analysis was conducted to evaluate the centrality and connectivity of the symptoms of anxiety, depression, quality of life (QoL) and insomnia. RESULTS: A total of 2806 respondents with complete GAD-7 and PHQ-9 scores out of 3828 were enrolled. The incidence of anxiety in the whole population was 26.5% (depression, 28.5%; either anxiety or depression, 34.8%). Anxiety was highest at caner diagnosis (34.2%), while depression reached a peak at late-stage radiotherapy (48.5%). Both moderate and severe anxiety and depression were exacerbated during radiotherapy. Coexisting anxiety and depression occurred in 58.3% of those with either anxiety or depression. The generated network showed that anxiety and depression symptoms were closely connected; insomnia was strongly connected with QoL. "Sad mood", "Lack of energy", and "Trouble relaxing" were the most important items in the network. Insomnia was the most significant bridge item that connected symptom groups. CONCLUSION: Patients with NPC are facing alarming disturbances of psychiatric disorders; tailored strategies should be implemented for high-risk patients. Besides, central symptoms (sad mood, lack of energy, and trouble relaxing) and bridge symptoms (insomnia) may be potential interventional targets in future clinical practice.

Telehealth Utilization and Associations in the United States During the Third Year of the COVID-19 Pandemic: Population-Based Survey Study in 2022.

BACKGROUND: The COVID-19 pandemic rapidly changed the landscape of clinical practice in the United States; telehealth became an essential mode of health care delivery, yet many components of telehealth use remain unknown years after the disease's emergence. OBJECTIVE: We aim to comprehensively assess telehealth use and its associated factors in the United States. METHODS: This cross-sectional study used a nationally representative survey (Health Information National Trends Survey) administered to US adults (≥18 years) from March 2022 through November 2022. To assess telehealth adoption, perceptions of telehealth, satisfaction with telehealth, and the telehealth care purpose, we conducted weighted descriptive analyses. To identify the subpopulations with low adoption of telehealth, we developed a weighted multivariable logistic regression model. RESULTS: Among a total of 6252 survey participants, 39.3% (2517/6252) reported telehealth use in the past 12 months (video: 1110/6252, 17.8%; audio: 876/6252, 11.6%). The most prominent reason for not using telehealth was due to telehealth providers failing to offer this option (2200/3529, 63%). The most common reason for respondents not using offered telehealth services was a preference for in-person care (527/578, 84.4%). Primary motivations to use telehealth were providers' recommendations (1716/2517, 72.7%) and convenience (1516/2517, 65.6%), mainly for acute minor illness (600/2397, 29.7%) and chronic condition management (583/2397, 21.4%), yet care purposes differed by age, race/ethnicity, and income. The satisfaction rate was predominately high, with no technical problems (1829/2517, 80.5%), comparable care quality to that of in-person care (1779/2517, 75%), and no privacy concerns (1958/2517, 83.7%). Younger individuals (odd ratios [ORs] 1.48-2.23; 18-64 years vs ≥75 years), women (OR 1.33, 95% CI 1.09-1.61), Hispanic individuals (OR 1.37, 95% CI 1.05-1.80; vs non-Hispanic White), those with more education (OR 1.72, 95% CI 1.03-2.87; at least a college graduate vs less than high school), unemployed individuals (OR 1.25, 95% CI 1.02-1.54), insured individuals (OR 1.83, 95% CI 1.25-2.69), or those with poor general health status (OR 1.66, 95% CI 1.30-2.13) had higher odds of using telehealth. CONCLUSIONS: To our best knowledge, this is among the first studies to examine patient factors around telehealth use, including motivations to use, perceptions of, satisfaction with, and care purpose of telehealth, as well as sociodemographic factors associated with telehealth adoption using a nationally representative survey. The wide array of descriptive findings and identified associations will help providers and health systems understand the factors that drive patients toward or away from telehealth visits as the technology becomes more routinely available across the United States, providing future directions for telehealth use and telehealth research.

Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis.

The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.

Transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical artificial intelligence (AI) systems requires the ability to interrogate data and models at all stages of the development pipeline, from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. In the present study, we present a foundation model approach, named MONET (medical concept retriever), which learns how to connect medical images with text and densely scores images on concept presence to enable important tasks in medical AI development and deployment such as data auditing, model auditing and model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones and imaging modalities. We trained MONET based on 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, competitively with supervised models built on previously concept-annotated dermatology datasets of clinical images. We demonstrate how MONET enables AI transparency across the entire AI system development pipeline, from building inherently interpretable models to dataset and model auditing, including a case study dissecting the results of an AI clinical trial.

Correction: Ji et al. Mesoporous Cobalt Oxide (CoOx) Nanowires with Different Aspect Ratios for High Performance Hybrid Supercapacitors. Nanomaterials 2023, 13, 749.

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Discovery of Tetrazolamide-benzimidazol-2-ones as Novel 4-Hydroxyphenylpyruvate Dioxygenase Inhibitors.

4-Hydroxyphenylpyruvate dioxygenase (HPPD, EC 1.13.11.27) is one of the most valuable herbicide targets due to its unique biological functions. In search of HPPD inhibitors with promising biological performance, we designed and synthesized a series of novel tetrazolamide-benzimidazol-2-ones using a structure-based drug design strategy. Among the synthesized compounds, 1-(2-chlorobenzyl)-3-methyl-N-(1-methyl-1H-tetrazol-5-yl)-2-oxo-2,3-dihydro-1H-benzo[d]imidazole-4-carboxamide, 25, IC50 = 10 nM, was identified to be the most outstanding HPPD inhibitor, which showed more than 36-fold increased Arabidopsis thaliana HPPD (AtHPPD) inhibition potency than mesotrione (IC50 = 363 nM). Our AtHPPD-25 complex indicated that one nitrogen atom on the tetrazole ring and the oxygen atom on the amide group formed a classical bidentate chelation interaction with the metal ion, the benzimidazol-2-one ring created a tight π-π stacking interaction with Phe381 and Phe424, and some hydrophobic interactions were also found between the ortho-Cl-benzyl group and surrounding residues. Compound 32 showed more than 80% inhibition against all four tested weeds at 150 g ai/ha by the postemergence application. Our results indicated that the tetrazolamide-benzimidazol-2-one scaffold may be a new lead structure for herbicide discovery.

Phylogenomic analyses reveal reticulate evolution between Neomicrocalamus and Temochloa (Poaceae: Bambusoideae).

Neomicrocalamus and Temochloa are closely related to bamboo genera. However, when considered with newly discovered and morphologically similar material from China and Vietnam, the phylogenetic relationship among these three groups was ambiguous in the analyses based on DNA regions. Here, as a means of investigating the relationships among the three bamboo groups and exploring potential sources of genomic conflicts, we present a phylogenomic examination based on the whole plastome, single-nucleotide polymorphism (SNP), and single-copy nuclear (SCN) gene datasets. Three different phylogenetic hypotheses were found. The inconsistency is attributed to the combination of incomplete lineage sorting and introgression. The origin of newly discovered bamboos is from introgressive hybridization between Temochloa liliana (which contributed 80.7% of the genome) and Neomicrocalamus prainii (19.3%), indicating that the newly discovered bamboos are closer to T. liliana in genetics. The more similar morphology and closer distribution elevation also imply a closer relationship between Temochloa and newly discovered bamboos.

Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.

The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from melanoma 'lookalikes' on the basis of dermoscopic and clinical images of the skin, and the power of generative models to render 'counterfactual' images to understand the 'reasoning' processes of five medical-image classifiers. By altering image attributes to produce analogous images that elicit a different prediction by the classifiers, and by asking physicians to identify medically meaningful features in the images, the counterfactual images revealed that the classifiers rely both on features used by human dermatologists, such as lesional pigmentation patterns, and on undesirable features, such as background skin texture and colour balance. The framework can be applied to any specialized medical domain to make the powerful inference processes of machine-learning models medically understandable.

Assessing Biases in Medical Decisions via Clinician and AI Chatbot Responses to Patient Vignettes.

This cross-sectional study compares clinician and artificial intelligence (AI) chatbot responses to patient vignettes used to identify bias in medical decisions.

Principles, applications, and future of artificial intelligence in dermatology.

This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption. Additionally, we discuss how the future of AI in dermatology might evolve and the emerging paradigm of large language, and multi-modal models to emphasize the importance of developing responsible, fair, and equitable models in dermatology.

Development of a digital tool for home-based monitoring of skin disease for older adults.

We developed a digital tool for home-based monitoring of skin disease, our digital tool. In the current observational pilot study, we found that DORA is feasible to use in practice, as it has a high patient compliance, retention and satisfaction. Clinicans rated the photos generally good quality or perfect quality. These results show that the digital health tool DORA can easily be used by patients to send photos to their dermatologist, which could reduce unnecessary clinical visits. It may also be used in other settings where digital literacy barriers and unequal access to dermatologists contribute to healthcare disparities.

A Social Media-Based Public Health Campaign to Reduce Indoor Tanning in High-Risk Populations.

Introduction: Indoor tanning beds cause more than 450,000 new skin cancers each year, yet their use remains common, with a global indoor tanning prevalence of 10.4%. Social media provides an opportunity for cost-effective, targeted public health messaging. We sought to direct Instagram users at high risk of indoor tanning to accurate health information about the risks of indoor tanning and to reduce indoor tanning bed use. Methods: We disseminated a public health campaign on Instagram on April 6-27, 2022 with 34 video and still-image advertisements. We had 2 target audiences at high risk of indoor tanning: women aged 18-30 years in Kentucky, Nebraska, Ohio, or Tennessee interested in indoor tanning and men aged 18-45 years in California interested in indoor tanning. To evaluate the impact of the campaign, we tracked online metrics, including website visits, and conducted an interrupted time-series analysis of foot traffic data in our target states for all tanning salons documented on SafeGraph from January 1, 2018 to 3 months after the campaign. Results: Our indoor tanning health information advertisements appeared on Instagram feeds 9.1 million times, reaching 1.06 million individuals. We received 7,004 views of our indoor tanning health information landing page (Average Time on Page of 56 seconds). We did not identify a significant impact on foot traffic data on tanning salons. Conclusions: We show the successful use of social media advertising to direct high-risk groups to online health information about indoor tanning. Future research quantifying tanning visits before and after indoor tanning interventions is needed to guide future public health efforts.

Risk of Multiple Primary Cancers in Patients With Merkel Cell Carcinoma: A SEER-Based Analysis.

Importance: The risk of subsequent primary cancers after a diagnosis of cutaneous Merkel cell carcinoma (MCC) is not well established. Objective: To evaluate the risk of subsequent primary cancers after the diagnosis of a first primary cutaneous MCC. Design, Setting, and Participants: This cohort study analyzed data from 17 registries of the Surveillance, Epidemiology, and End Results (SEER) Program from January 1, 2000, to December 31, 2019. In all, 6146 patients diagnosed with a first primary cutaneous MCC were identified. Main Outcomes and Measures: The primary outcome was the relative and absolute risks of subsequent primary cancers after the diagnosis of a first primary MCC, which were calculated using the standardized incidence ratio (SIR; ratio of observed to expected cases of subsequent cancer) and the excess risk (difference between observed and expected cases of subsequent cancer divided by the person-years at risk), respectively. Data were analyzed between January 1, 2000, and December 31, 2019. Results: Of 6146 patients with a first primary MCC diagnosed at a median (IQR) age of 76 (66-83) years, 3713 (60.4%) were men, and the predominant race and ethnicity was non-Hispanic White (5491 individuals [89.3%]). Of these patients, 725 (11.8%) developed subsequent primary cancers, with an SIR of 1.28 (95% CI, 1.19-1.38) and excess risk of 57.25 per 10 000 person-years. For solid tumors after MCC, risk was elevated for cutaneous melanoma (SIR, 2.36 [95% CI, 1.85-2.97]; excess risk, 15.27 per 10 000 person-years) and papillary thyroid carcinoma (SIR, 5.26 [95% CI, 3.25-8.04]; excess risk, 6.16 per 10 000 person-years). For hematologic cancers after MCC, risk was increased for non-Hodgkin lymphoma (SIR, 2.62 [95% CI, 2.04-3.32]; excess risk, 15.48 per 10 000 person-years). Conclusions and Relevance: This cohort study found that patients with MCC had an increased risk of subsequently developing solid and hematologic cancers. This increased risk may be associated with increased surveillance, treatment-related factors, or shared etiologies of the other cancers with MCC. Further studies exploring possible common etiological factors shared between MCC and other primary cancers are warranted.

Fostering transparent medical image AI via an image-text foundation model grounded in medical literature.

Building trustworthy and transparent image-based medical AI systems requires the ability to interrogate data and models at all stages of the development pipeline: from training models to post-deployment monitoring. Ideally, the data and associated AI systems could be described using terms already familiar to physicians, but this requires medical datasets densely annotated with semantically meaningful concepts. Here, we present a foundation model approach, named MONET (Medical cONcept rETriever), which learns how to connect medical images with text and generates dense concept annotations to enable tasks in AI transparency from model auditing to model interpretation. Dermatology provides a demanding use case for the versatility of MONET, due to the heterogeneity in diseases, skin tones, and imaging modalities. We trained MONET on the basis of 105,550 dermatological images paired with natural language descriptions from a large collection of medical literature. MONET can accurately annotate concepts across dermatology images as verified by board-certified dermatologists, outperforming supervised models built on previously concept-annotated dermatology datasets. We demonstrate how MONET enables AI transparency across the entire AI development pipeline from dataset auditing to model auditing to building inherently interpretable models.

Dissection of medical AI reasoning processes via physician and generative-AI collaboration.

Despite the proliferation and clinical deployment of artificial intelligence (AI)-based medical software devices, most remain black boxes that are uninterpretable to key stakeholders including patients, physicians, and even the developers of the devices. Here, we present a general model auditing framework that combines insights from medical experts with a highly expressive form of explainable AI that leverages generative models, to understand the reasoning processes of AI devices. We then apply this framework to generate the first thorough, medically interpretable picture of the reasoning processes of machine-learning-based medical image AI. In our synergistic framework, a generative model first renders "counterfactual" medical images, which in essence visually represent the reasoning process of a medical AI device, and then physicians translate these counterfactual images to medically meaningful features. As our use case, we audit five high-profile AI devices in dermatology, an area of particular interest since dermatology AI devices are beginning to achieve deployment globally. We reveal how dermatology AI devices rely both on features used by human dermatologists, such as lesional pigmentation patterns, as well as multiple, previously unreported, potentially undesirable features, such as background skin texture and image color balance. Our study also sets a precedent for the rigorous application of explainable AI to understand AI in any specialized domain and provides a means for practitioners, clinicians, and regulators to uncloak AI's powerful but previously enigmatic reasoning processes in a medically understandable way.

Widely targeted quantitative lipidomics and prognostic model reveal plasma lipid predictors for nasopharyngeal carcinoma.

BACKGROUND: Dysregulation of lipid metabolism is closely associated with cancer progression. The study aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC), based on lipidomics. METHODS: The plasma lipid profiles of 179 patients with locoregionally advanced NPC (LANPC) were measured and quantified using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set (P < 0.05). A deep survival method called DeepSurv was employed to develop a proposed model based on significant lipid species (P < 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. The study also explored the potential role of lipid alterations in the prognosis of NPC. RESULTS: Forty lipids were recognized as distant metastasis-associated (P < 0.05) by univariate Cox regression. The concordance indices of the proposed model were 0.764 (95% confidence interval (CI), 0.682-0.846) and 0.760 (95% CI, 0.649-0.871) in the training and validation sets, respectively. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52-194.80; P < 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways. CONCLUSIONS: Widely targeted quantitative lipidomics reveals plasma lipid predictors for LANPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in LANPC patients.

Fast-response photothermal bilayer actuator based on poly(N-isopropylacrylamide)-graphene oxide-hydroxyethyl methacrylate/polydimethylsiloxane.

Demands for highly deformable and responsive intelligent actuators are increasing rapidly. Herein, a photothermal bilayer actuator consisting of a photothermal-responsive composite hydrogel layer and a polydimethylsiloxane (PDMS) layer is presented. The photothermal-responsive composite hydrogel is prepared by compositing hydroxyethyl methacrylate (HEMA) and the photothermal material graphene oxide (GO) with the thermal-responsive hydrogel poly(N-isopropylacrylamide) (PNIPAM). The HEMA improves the transport efficiency of water molecules inside the hydrogel network, eliciting a fast response and large deformation, facilitating greater bending behavior of the bilayer actuator, and improving the mechanical and tensile properties of the hydrogel. Moreover, GO enhances the mechanical properties and the photothermal conversion efficiency of the hydrogel in the thermal environment. This photothermal bilayer actuator can be driven under various conditions, such as hot solution, simulated sunlight, and laser, and can achieve large bending deformation with desirable tensile properties, broadening the application conditions for bilayer actuators, such as artificial muscles, bionic actuators, and soft robotics.

Tumor residue in patients with stage II-IVA nasopharyngeal carcinoma who received intensity-modulated radiation therapy: development and validation of a prediction nomogram integrating postradiotherapy plasma Epstein-Barr virus deoxyribonucleic acid, clinical stage, and radiotherapy dose.

BACKGROUND: To develop and validate a predictive nomogram for tumor residue 3-6 months after treatment based on postradiotherapy plasma Epstein-Barr virus (EBV) deoxyribonucleic acid (DNA), clinical stage, and radiotherapy (RT) dose in patients with stage II-IVA nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiation therapy (IMRT). METHODS: In this retrospective study, 1050 eligible patients with stage II-IVA NPC, who completed curative IMRT and underwent pretreatment and postradiotherapy (-7 to +28 days after IMRT) EBV DNA testing, were enrolled from 2012 to 2017. The prognostic value of the residue was explored using Cox regression analysis in patients (n=1050). A nomogram for predicting tumor residues after 3-6 months was developed using logistic regression analyses in the development cohort (n=736) and validated in an internal cohort (n=314). RESULTS: Tumor residue was an independent inferior prognostic factor for 5-year overall survival, progression-free survival, locoregional recurrence-free survival and distant metastasis-free survival (all P<0.001). A prediction nomogram based on postradiotherapy plasma EBV DNA level (0 vs. 1-499 vs. ≥500 copies/ml), clinical stage (II vs. III vs. IVA), and RT dose (68.00-69.96 vs. 70.00-74.00 Gy) estimated the probability of residue development. The nomogram showed better discrimination (area under the curve (AUC): 0.752) than either the clinical stage (0.659) or postradiotherapy EBV DNA level (0.627) alone in the development and validation cohorts (AUC: 0.728). CONCLUSIONS: We developed and validated a nomogram model integrating clinical characteristics at the end of IMRT for predicting whether tumor will residue or not after 3-6 months. Thus, high-risk NPC patients who might benefit from immediate additional intervention could be identified by the model, and the probability of residue can be reduced in the future.