A Study on Voice Measures in Patients with Parkinson's Disease.

PURPOSE: This research aims to identify acoustic features which can distinguish patients with Parkinson's disease (PD patients) and healthy speakers. METHODS: Thirty PD patients and 30 healthy speakers were recruited in the experiment, and their speech was collected, including three vowels (/i/, /a/, and /u/) and nine consonants (/p/, /pÊ°/, /t/, /tÊ°/, /k/, /kÊ°/, /l/, /m/, and /n/). Acoustic features like fundamental frequency (F0), Jitter, Shimmer, harmonics-to-noise ratio (HNR), first formant (F1), second formant (F2), third formant (F3), first bandwidth (B1), second bandwidth (B2), third bandwidth (B3), voice onset, voice onset time were analyzed in our experiment. Two-sample independent t test and the nonparametric Mann-Whitney U (MWU) test were carried out alternatively to compare the acoustic measures between the PD patients and healthy speakers. In addition, after figuring out the effective acoustic features for distinguishing PD patients and healthy speakers, we adopted two methods to detect PD patients: (1) Built classifiers based on the effective acoustic features and (2) Trained support vector machine classifiers via the effective acoustic features. RESULTS: Significant differences were found between the male PD group and the male health control in vowel /i/ (Jitter and Shimmer) and /a/ (Shimmer and HNR). Among female subjects, significant differences were observed in F0 standard deviation (F0 SD) of /u/ between the two groups. Additionally, significant differences between PD group and health control were also found in the F3 of /i/ and /n/, whereas other acoustic features showed no significant differences between the two groups. The HNR of vowel /a/ performed the best classification accuracy compared with the other six acoustic features above found to distinguish PD patients and healthy speakers. CONCLUSIONS: PD can cause changes in the articulation and phonation of PD patients, wherein increases or decreases occur in some acoustic features. Therefore, the use of acoustic features to detect PD is expected to be a low-cost and large-scale diagnostic method.

Gender differences in risk factors for ischemic stroke: a longitudinal cohort study in East China.

OBJECTIVES: Epidemiological studies of stroke and its risk factors can help develop strategies to prevent stroke. We aimed to explore the current gender-specific prevalence of stroke and associated risk factors. METHODS: Data were collected using a structured precoded questionnaire designed by the Stroke Screening and Prevention Programme of the National Health and Wellness Commission Stroke Prevention and Control Project Committee, between June 2020 and November 2021. A total of 7394 residents took part in the study, 187 of whom had a stroke. The baseline information of each participant was obtained and included in this study. The chi-square test and Kruskal-Wallis tests were used to examine the relationship between these indicators and stroke, and then multivariate logistic regression was used to construct the prediction scale between different genders. RESULTS: of 7394 participants,4571 (61.82%) were female. The overall prevalence of stroke patients in the study population was 2.53%, Multivariate analysis found that residence status (OR = 0.43, p = 0.002) 、HCY (OR = 0.962, p = 0.000)、Previous TIA (OR = 0.200, p = 0.002) 、Hypertension (OR = 0.33, p = 0.000) and Dyslipidemia (OR = 0.668, p = 0.028) were significant predictors of stroke. there are gender differences in the traditional risk factors for stroke, and women have more risk factors. ROC analysis confirmed the accuracy of the stroke risk model, and the AUC of the stroke risk model for the general population was 0.79 with p < 0.05. In the gender model, the female AUC was 0.796 (p < 0.05). and the male AUC was 0.786 with p < 0.05. CONCLUSION: The prevalence of stroke in adults aged 40 years and above is high in eastern China were high. management of risk factors can effectively prevent the occurrence of most strokes. more attention should be paid to gender differences associated with stroke.

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.

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.

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.

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

There was an error in the original publication [...].

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.

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.

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.

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.

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.

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.

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.

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.