Multiparametric magnetic resonance imaging-based assessment of the effect of adenomyosis on determining the depth of myometrial invasion in endometrial cancer.

Background: Accurate preoperative diagnosis of endometrial cancer (EC) with deep myometrial invasion (DMI) is critical to deciding whether to perform lymphadenectomy. However, the presence of adenomyosis makes distinguishing DMI from superficial myometrial invasion (SMI) on magnetic resonance imaging (MRI) challenging. We aimed to evaluate the accuracy of multiparametric MRI (mpMRI) in diagnosing DMI in EC coexisting with adenomyosis (EC-A) compared with EC without coexisting adenomyosis and to evaluate the effect of different adenomyosis subtypes on myometrial invasion (MI) depth in EC. Methods: Patients with histologically confirmed International Federation of Gynecology and Obstetrics (FIGO) stage I EC who underwent preoperative MRI were consecutively included in this 2-center retrospective study. Institution 1 was searched from January 2017 to November 2022 and institution 2 was searched from June 2017 to March 2021. Patients were divided into 2 groups: group A, patients with EC-A; group B, EC patients without coexisting adenomyosis, matched 1:2 according to age ±5 years and tumor grade. A senior radiologist assessed the MRI adenomyosis classification in group A. Then, 2 radiologists (R1/R2) independently interpreted T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), T1-weighted contrast-enhanced (T1CE), and a combination of all images (mpMRI) respectively, and then assessed MI depth. Accuracy, sensitivity, specificity, and the areas under the receiver operating curve (AUC) were calculated. The chi-square test was used to compare the accuracy of diagnosing DMI. Interobserver agreement was evaluated using the Kappa test. Results: A total of 70 cases in group A and 140 cases in group B were included. The accuracy, sensitivity, and specificity of consensus were 94.3% [95% confidence interval (CI): 88.9-99.7%] vs. 92.1% (95% CI: 87.7-96.6%), 60.0% (95% CI: 17-92.7%) vs. 86.7% (95% CI: 68.4-95.6%), and 96.9% (95% CI: 88.4-95.5%) vs. 93.6% (95% CI: 86.8-97.2%) (group A vs. group B, respectively). There was no significant difference in the diagnostic accuracy of DMI on each sequence between the groups (Reviewer 1/Reviewer 2): PT2WI=0.14/0.17, PDWI=0.50/0.33, PT1CE=0.90/0.18, PmpMRI=0.50/0.37. The AUC for T2WI, DWI, T1CE, and mpMRI (Reviewer 1/Reviewer 2), respectively, were 0.54 (95% CI: 0.42-0.66)/0.78 (95% CI: 0.67-0.87), 0.63 (95% CI: 0.50-0.74)/0.77 (95% CI: 0.65-0.86), 0.69 (95% CI: 0.57-0.80)/0.79 (95% CI: 0.68-0.88), and 0.91 (95% CI: 0.82-0.97)/0.89 (95% CI: 0.79-0.95) (group A) and 0.83 (95% CI: 0.76-0.89)/0.85 (95% CI: 0.78-0.90), 0.83 (95% CI: 0.76-0.89)/0.86 (95% CI: 0.79-0.91), 0.88 (95% CI: 0.82-0.93)/0.86 (95% CI: 0.80-0.92), and 0.91 (95% CI: 0.85-0.95)/0.87 (95% CI: 0.80-0.92) (group B). Interobserver agreement was highest with mpMRI [κ=0.387/0.695 (case/control)]. The consensus results of MRI categorization of adenomyosis revealed no significant difference in the accuracy of diagnosing DMI by adenomyosis subtype (Pspatial relationship>0.99, Paffected area=0.52, Paffected pattern=0.58, Paffected size>0.99). Conclusions: The presence of adenomyosis or adenomyosis subtype had no significant effect on the interpretation of the depth of MI. T1CE can increase the contrast between adenomyosis and cancer foci; therefore, the information provided by T1CE should be valued.

Integrating Reinforcement Learning and Monte Carlo Tree Search for enhanced neoantigen vaccine design.

Recent advances in cancer immunotherapy have highlighted the potential of neoantigen-based vaccines. However, the design of such vaccines is hindered by the possibility of weak binding affinity between the peptides and the patient's specific human leukocyte antigen (HLA) alleles, which may not elicit a robust adaptive immune response. Triggering cross-immunity by utilizing peptide mutations that have enhanced binding affinity to target HLA molecules, while preserving their homology with the original one, can be a promising avenue for neoantigen vaccine design. In this study, we introduced UltraMutate, a novel algorithm that combines Reinforcement Learning and Monte Carlo Tree Search, which identifies peptide mutations that not only exhibit enhanced binding affinities to target HLA molecules but also retains a high degree of homology with the original neoantigen. UltraMutate outperformed existing state-of-the-art methods in identifying affinity-enhancing mutations in an independent test set consisting of 3660 peptide-HLA pairs. UltraMutate further showed its applicability in the design of peptide vaccines for Human Papillomavirus and Human Cytomegalovirus, demonstrating its potential as a promising tool in the advancement of personalized immunotherapy.

Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study.

Background: Artificial intelligence (AI) has repeatedly been shown to encode historical inequities in healthcare. We aimed to develop a framework to quantitatively assess the performance equity of health AI technologies and to illustrate its utility via a case study. Methods: Here, we propose a methodology to assess whether health AI technologies prioritise performance for patient populations experiencing worse outcomes, that is complementary to existing fairness metrics. We developed the Health Equity Assessment of machine Learning performance (HEAL) framework designed to quantitatively assess the performance equity of health AI technologies via a four-step interdisciplinary process to understand and quantify domain-specific criteria, and the resulting HEAL metric. As an illustrative case study (analysis conducted between October 2022 and January 2023), we applied the HEAL framework to a dermatology AI model. A set of 5420 teledermatology cases (store-and-forward cases from patients of 20 years or older, submitted from primary care providers in the USA and skin cancer clinics in Australia), enriched for diversity in age, sex and race/ethnicity, was used to retrospectively evaluate the AI model's HEAL metric, defined as the likelihood that the AI model performs better for subpopulations with worse average health outcomes as compared to others. The likelihood that AI performance was anticorrelated to pre-existing health outcomes was estimated using bootstrap methods as the probability that the negated Spearman's rank correlation coefficient (i.e., "R") was greater than zero. Positive values of R suggest that subpopulations with poorer health outcomes have better AI model performance. Thus, the HEAL metric, defined as p (R >0), measures how likely the AI technology is to prioritise performance for subpopulations with worse average health outcomes as compared to others (presented as a percentage below). Health outcomes were quantified as disability-adjusted life years (DALYs) when grouping by sex and age, and years of life lost (YLLs) when grouping by race/ethnicity. AI performance was measured as top-3 agreement with the reference diagnosis from a panel of 3 dermatologists per case. Findings: Across all dermatologic conditions, the HEAL metric was 80.5% for prioritizing AI performance of racial/ethnic subpopulations based on YLLs, and 92.1% and 0.0% respectively for prioritizing AI performance of sex and age subpopulations based on DALYs. Certain dermatologic conditions were significantly associated with greater AI model performance compared to a reference category of less common conditions. For skin cancer conditions, the HEAL metric was 73.8% for prioritizing AI performance of age subpopulations based on DALYs. Interpretation: Analysis using the proposed HEAL framework showed that the dermatology AI model prioritised performance for race/ethnicity, sex (all conditions) and age (cancer conditions) subpopulations with respect to pre-existing health disparities. More work is needed to investigate ways of promoting equitable AI performance across age for non-cancer conditions and to better understand how AI models can contribute towards improving equity in health outcomes. Funding: Google LLC.

SERPINA3, FGA, AGP1, ITIH3 and SAA1 as novel biomarkers for eosinophilic granulomatosis with polyangiitis diagnosis and activity assessment.

OBJECTIVE: To identify novel biomarkers for diagnosis and prediction of active eosinophilic granulomatosis with polyangiitis (EGPA) through data-independent acquisition (DIA) analysis. METHODS: Plasma from 11 EGPA patients and 10 healthy controls (HCs) were analyzed through DIA to identify potential biomarkers. The results were validated in 32 EGPA patients, 24 disease controls (DCs), and 20 HCs using enzyme-linked immunosorbent assay (ELISA). The receiver operating characteristic (ROC) curve was used to assess the diagnostic value of candidate biomarkers. RESULTS: Thirty-five differentially expressed proteins (DEPs) (24 upregulated and 11 downregulated) were screened between EGPA and HC groups. Five proteins, including serine proteinase inhibitor A3 (SERPINA3), alpha-fibrinogen (FGA), alpha-1 acid glycoprotein 1(AGP1), inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3), and serum amyloid A1 (SAA1), were significantly upregulated in EGPA compared with HCs. Apart from SAA1, all proteins were also higher in EGPA patients compared with DCs. Furthermore, a panel of SERPINA3 and SAA1 exhibited potential diagnostic value for EGPA with an area under the curve (AUC) of 0.953, while a panel of SERPINA3, FGA, AGP1, and ITIH3 showed good discriminative power to differentiate EGPA from DCs with AUC of 0.926. Moreover, SERPINA3, FGA, and AGP levels were significantly higher in active EGPA and correlated well with disease activity. A combination of SERPINA3 and AGP1 exhibited an excellent AUC of 0.918 for disease activity assessment. CONCLUSION: SERPINA3, FGA, AGP1, ITIH3 and SAA1 were identified as potential biomarkers for EGPA diagnosis and disease activity assessment. Among them, as a single biomarker, SERPINA3 has the best diagnostic performance.

An intentional approach to managing bias in general purpose embedding models.

Advances in machine learning for health care have brought concerns about bias from the research community; specifically, the introduction, perpetuation, or exacerbation of care disparities. Reinforcing these concerns is the finding that medical images often reveal signals about sensitive attributes in ways that are hard to pinpoint by both algorithms and people. This finding raises a question about how to best design general purpose pretrained embeddings (GPPEs, defined as embeddings meant to support a broad array of use cases) for building downstream models that are free from particular types of bias. The downstream model should be carefully evaluated for bias, and audited and improved as appropriate. However, in our view, well intentioned attempts to prevent the upstream components-GPPEs-from learning sensitive attributes can have unintended consequences on the downstream models. Despite producing a veneer of technical neutrality, the resultant end-to-end system might still be biased or poorly performing. We present reasons, by building on previously published data, to support the reasoning that GPPEs should ideally contain as much information as the original data contain, and highlight the perils of trying to remove sensitive attributes from a GPPE. We also emphasise that downstream prediction models trained for specific tasks and settings, whether developed using GPPEs or not, should be carefully designed and evaluated to avoid bias that makes models vulnerable to issues such as distributional shift. These evaluations should be done by a diverse team, including social scientists, on a diverse cohort representing the full breadth of the patient population for which the final model is intended.

Revealed versus potential spatial accessibility of healthcare and changing patterns during the COVID-19 pandemic.

BACKGROUND: Timely access to healthcare is essential but measuring access is challenging. Prior research focused on analyzing potential travel times to healthcare under optimal mobility scenarios that do not incorporate direct observations of human mobility, potentially underestimating the barriers to receiving care for many populations. METHODS: We introduce an approach for measuring accessibility by utilizing travel times to healthcare facilities from aggregated and anonymized smartphone Location History data. We measure these revealed travel times to healthcare facilities in over 100 countries and juxtapose our findings with potential (optimal) travel times estimated using Google Maps directions. We then quantify changes in revealed accessibility associated with the COVID-19 pandemic. RESULTS: We find that revealed travel time differs substantially from potential travel time; in all but 4 countries this difference exceeds 30 minutes, and in 49 countries it exceeds 60 minutes. Substantial variation in revealed healthcare accessibility is observed and correlates with life expectancy (⍴=-0.70) and infant mortality (⍴=0.59), with this association remaining significant after adjusting for potential accessibility and wealth. The COVID-19 pandemic altered the patterns of healthcare access, especially for populations dependent on public transportation. CONCLUSIONS: Our metrics based on empirical data indicate that revealed travel times exceed potential travel times in many regions. During COVID-19, inequitable accessibility was exacerbated. In conjunction with other relevant data, these findings provide a resource to help public health policymakers identify underserved populations and promote health equity by formulating policies and directing resources towards areas and populations most in need.

Spatial access to healthcare facilities (i.e., how long people need to travel to reach care) is important for understanding public health, but hard to measure. Most research so far has focused on theoretical (potential) travel times. Using anonymized smartphone location history data, we measure actual (revealed) travel times to healthcare facilities in over 100 countries. We find that revealed travel times exceed theoretical travel times in many regions of the world, meaning that in reality people travel longer to get healthcare. Our data also show that inequities in travel time became worse during the COVID-19 pandemic. When combined with other data, these results can help policymakers identify areas and populations at need, and direct resources to improve public health.

Real-World Utilization, Barriers, and Factors Associated With the Targeted Treatment of Metastatic Colorectal Cancer Patients in China: A Multi-Center, Hospital-Based Survey Study.

Objectives: To explore the utilization, barriers, and factors associated with the targeted treatment of Chinese metastatic colorectal cancer (mCRC) patients. Methods: A total of 1,688 mCRC patients from 19 hospitals in 14 cities were enrolled from March 2020 to March 2021 using stratified, multistage cluster sampling. The use of targeted therapy and any barriers patients experienced were collected. Logistic regression analyses were conducted to identify the factors associated with initiating targeted treatment. Results: About 51.6% of the patients initiated targeted therapy, of whom 44.5%, 20.2%, and 35.2% started first-, second-, and third-line treatment, respectively. The most reported barriers were high medical costs and a lack of belief in the efficacy of targeted therapy. Patients treated in the general hospital, diagnosed at an older age, less educated, and who had a lower family income, no medical insurance, poor health-related quality of life, metastasis outside the liver/lung or systemic metastasis, a shorter duration of mCRC were less likely to initiate targeted therapy. Conclusion: Reduced medical costs and interventional education to improve public awareness could facilitate the use of targeted treatment for mCRC.

Global, regional, and national time trends in cancer mortality attributable to high fasting plasma glucose: an age-period cohort analysis.

BACKGROUND: High fasting plasma glucose (HFPG) is the fastest-growing risk factor for cancer deaths worldwide. We reported the cancer mortality attributable to HFPG at global, regional, and national levels over the past three decades and associations with age, period, and birth cohort. METHODS: Data for this study were retrieved from the Global Burden of Disease Study 2019, and we used age-period-cohort modelling to estimate age, cohort and period effects, as well as net drift (overall annual percentage change) and local drift (annual percentage change in each age group). RESULTS: Over the past 30 years, the global age-standardized mortality rate (ASMR) attributable to HFPG has increased by 27.8%. The ASMR in 2019 was highest in the male population in high sociodemographic index (SDI) areas (8.70; 95% CI, 2.23-18.04). The net drift for mortality was highest in the female population in low SDI areas (2.33; 95% CI, 2.12-2.55). Unfavourable period and cohort effects were found across all SDI quintiles. Cancer subtypes such as "trachea, bronchus, and lung cancers", "colon and rectal cancers", "breast cancer" and "pancreatic cancer" exhibited similar trends. CONCLUSIONS: The cancer mortality attributable to HFPG has surged during the past three decades. Unfavourable age-period-cohort effects on mortality were observed across all SDI quintiles, and the cancer mortality attributable to HFPG is expected to continue to increase rapidly in the future, particularly in lower SDI locations. This is a grim global public health issue that requires immediate attention.

Burden of Knee Osteoarthritis in 204 Countries and Territories, 1990-2019: Results From the Global Burden of Disease Study 2019.

OBJECTIVE: To report the global, regional, and national estimates of knee osteoarthritis (OA) burden and associated risk factors (high body mass index [BMI]) by age, sex, and sociodemographic index (SDI) for 204 countries from 1990 to 2019. METHODS: We analyzed the prevalence, incidence, years lived with disability (YLDs), and age-standardized rates of knee OA using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019. Estimates of the knee OA burden were derived from data modeled using a Bayesian meta-regression analytical tool (DisMod-MR 2.1). RESULTS: The global prevalence of knee OA in 2019 was ~364.6 million (95% uncertainty interval [95% UI] 315.3 to 417.4). The age-standardized prevalence in 2019 was 4,376.0 per 100,000 (95% UI 3,793.0 to 5,004.9), an increase of 7.5% between 1990 and 2019. There were ~29.5 million incident cases of knee OA in 2019 (95% UI 25.6 to 33.7), with an age-standardized incidence of 350.3 per 100,000 (95% UI 303.4 to 398.9). The global age-standardized YLD resulting from knee OA was 138.2 (95% UI 68.5 to 281.3) per 100,000 population in 2019, an increase of 7.8% (95% UI 7.1 to 8.4) from 1990. Globally in 2019, 22.4% (95% UI 12.1 to 34.2) of YLD resulting from knee OA was attributable to high BMI, an increase of 40.5% since 1990. CONCLUSION: The prevalence, incidence, YLDs, and age-standardized rates of knee OA increased substantially in most countries and regions from 1990 to 2019. Continuous monitoring of this burden is important for establishing appropriate public prevention policies and raising public awareness, especially in high- and high-middle SDI regions.

Secular trends and urban-rural disparities in the median age at menarche among Chinese han girls from 1985 to 2019.

BACKGROUND: Menarche is a substantial milestone of female puberty. Timing of age at menarche is considered the key to understanding the potential linkages with women's health outcomes later in life. This study aimed to explore the secular trends and urban‒rural disparities in the median age at menarche among Chinese Han girls from 1985 to 2019. METHODS: Data were extracted from the 1985, 1995, 2005, 2014, and 2019 Chinese National Surveys on Students' Constitution and Health, which were nationally representative cross-sectional studies, and a total of 173,535 Han girls aged 9-18 years were examined. Girls were asked whether menarche had occurred. The median age at menarche was estimated by probit analysis. Z tests were used to compare the differences between survey years and between urban and rural areas. RESULTS: The median age at menarche among Chinese Han girls decreased from 13.37 years in 1985 to 12.00 years in 2019, and the overall decrease was more significant in rural areas (1.77 years) than in urban areas (0.99 years). The average five-year change in the decrease in the median age at menarche showed an accelerating and then slowing pace; and it was observed similarly in both urban and rural areas. The urban‒rural disparities shrank from 0.64 years in 1985 to 0.44 years in 1995, then to 0.27 years in 2005, 0.24 years in 2014, and finally to - 0.14 years in 2019. CONCLUSIONS: The median age at menarche among Chinese Han girls continued to decline from 1985 to 2019 but at a slowing pace in the last five years. Urban‒rural disparities gradually narrowed. Sexual and reproductive health education and interventions to prevent the decline in the age of menarche are needed, especially in rural areas.

The toxicity assessment of neorudin in cynomolgus monkeys.

In this study, the toxicity effects on circulatory system and respiratory system, and the acute toxicity test of recombinant neorudin (EPR-hirudin, EH) in cynomolgus monkeys were evaluated to provide reference information for clinical studies. Eighteen cynomolgus monkeys were randomly divided into three groups for single intravenous administration of 3, 30 mg/kg EH and normal saline, respectively. The changes of respiratory frequency, respiratory intensity, blood pressure and electrocardiogram before and after administration were recorded. In acute toxicity test, six cynomolgus monkeys were intravenously received EH at a single dose of 171, 257, 385, 578, 867 and 1300 mg/kg respectively. The vital signs, hematology, serum biochemistry, coagulation indexes and electrocardiogram indexes of the animals were determined before administration and on the 7th and 14th day after administration. As the results showed that there were no significant abnormal changes in respiratory frequency, respiratory intensity, blood pressure or electrocardiogram in cynomolgus monkeys after receiving EH at 3 mg/kg and 30 mg/kg, and there was no statistical difference between the treated groups and normal saline group. In the acute toxicity test, no significant abnormalities were observed in vital signs, hematology, serum biochemistry, coagulation indexes and electrocardiogram indexes of six cynomolgus monkeys at day 7 and 14 after EH administration. Furthermore, autopsies of all cynomolgus monkeys showed no abnormalities. The results of toxicokinetics showed that AUClast of the drug increased in proportion to the EH dose in the range of 171-578 mg/kg, and increased in over proportion to the EH dose in the range of 578-1300 mg/kg. The variation of Cmax was basically consistent with AUClast. In a sum, A single intravenous injection of 3 and 30 mg/kg of EH did not affect the circulatory system and respiratory system in cynomolgus monkeys and the maximum tolerated dose of EH in cynomolgus monkey is over 1300 mg/kg (equivalent to 619-1300 times of the proposed clinical equivalent dose).

Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program.

INTRODUCTION: Deep learning (DL) for screening diabetic retinopathy (DR) has the potential to address limited healthcare resources by enabling expanded access to healthcare. However, there is still limited health economic evaluation, particularly in low- and middle-income countries, on this subject to aid decision-making for DL adoption. METHODS: In the context of a middle-income country (MIC), using Thailand as a model, we constructed a decision tree-Markov hybrid model to estimate lifetime costs and outcomes of Thailand's national DR screening program via DL and trained human graders (HG). We calculated the incremental cost-effectiveness ratio (ICER) between the two strategies. Sensitivity analyses were performed to probe the influence of modeling parameters. RESULTS: From a societal perspective, screening with DL was associated with a reduction in costs of ~ US$ 2.70, similar quality-adjusted life-years (QALY) of + 0.0043, and an incremental net monetary benefit of ~ US$ 24.10 in the base case. In sensitivity analysis, DL remained cost-effective even with a price increase from US$ 1.00 to US$ 4.00 per patient at a Thai willingness-to-pay threshold of ~ US$ 4.997 per QALY gained. When further incorporating recent findings suggesting improved compliance to treatment referral with DL, our analysis models effectiveness benefits of ~ US$ 20 to US$ 50 depending on compliance. CONCLUSION: DR screening using DL in an MIC using Thailand as a model may result in societal cost-savings and similar health outcomes compared with HG. This study may provide an economic rationale to expand DL-based DR screening in MICs as an alternative solution for limited availability of skilled human resources for primary screening, particularly in MICs with similar prevalence of diabetes and low compliance to referrals for treatment.

Quality assessment of rose tea with different drying methods based on physicochemical properties, HS-SPME-GC-MS, and GC-IMS.

The purpose of this study is to compare the physicochemical properties and volatile flavor compounds of rose tea obtained by the methods of normal temperature drying, hot-air drying (HAD), and vacuum freeze-drying (VFD) and to evaluate the quality of rose tea. The physicochemical results showed that the content of ascorbic acid (VC) and the pH value was the highest in rose tea obtained by HAD. The contents of anthocyanin, proanthocyanidins, and total phenols were highest in rose tea obtained by VFD. However, there was no significant difference in total flavonoids between drying methods. The volatile organic compounds (VOCs) in rose tea with different drying methods were analyzed by headspace solid-phase microextraction-gas chromatography-mass spectroscopy (HS-SPME-GC-MS) and HS GC-ion mobility spectroscopy (HS-GC-IMS), and the flavor fingerprint of rose tea was established by principal component analysis (PCA). The concentration of VOCs in rose tea varied greatly with different drying methods. The main flavor compounds of rose tea were alcohols, esters, aldehydes, and terpenoids. HS-GC-IMS was used for the identification of volatile flavor compounds of rose tea, thereby helping to assess the quality of rose tea. In addition, the rose tea samples with different drying methods were well distinguished by PCA. This study deepens the understanding of the physicochemical properties and volatile flavor compounds of rose tea with different drying methods and provides a reference for the identification of rose tea with different drying methods. PRACTICAL APPLICATION: This study deepens the understanding of the physicochemical properties and volatile flavor compounds of rose tea with different drying methods and provides a reference for the identification of rose tea with different drying methods. It also provides an effective theoretical basis for consumers to buy rose tea.

Marine Structural Health Monitoring with Optical Fiber Sensors: A Review.

Real-time monitoring of large marine structures' health, including drilling platforms, submarine pipelines, dams, and ship hulls, is greatly needed. Among the various kinds of monitoring methods, optical fiber sensors (OFS) have gained a lot of concerns and showed several distinct advantages, such as small size, high flexibility and durability, anti-electromagnetic interference, and high transmission rate. In this paper, three types of OFS used for marine structural health monitoring (SHM), including point sensing, quasi-distributed sensing, and distributed sensing, are reviewed. Emphases are given to the applicability of each type of the sensors by analyzing the operating principles and characteristics of the OFSs. The merits and demerits of different sensing schemes are discussed, as well as the challenges and future developments in OFSs for the marine SHM field.

Impact of pharmaceutical care pathway established in hepatobiliary surgery.

OBJECTIVES: Clinical pharmacists play a pivotal role in ensuring medication safety due to their detailed understanding of the medication-use process. This study aimed to propose the concept of pharmaceutical care pathway (PCP) in surgical care and design the work pattern and workflow in the healthcare systems of China. SETTING: Data were collected from patients in the Department of Hepatobiliary Surgery of the First People's Hospital of Lianyungang in China between January 2019 and December 2019. MATERIALS AND METHODS: The study was conducted using 346 patients in the control group and 363 in the intervention group. The control group was managed only by the clinical pathway (CP), while the intervention group was managed by the CP and PCP. MAIN OUTCOME MEASURE: Adverse drug reactions (ADRs), patient satisfaction, hospital expense, drug cost, length of stay, and prescription situations were documented. RESULTS: Using PCP, the rational use of drugs increased from 56% in the control group to 94.2% in the intervention group. Further, 124 (35.8%) ADRs in the control group and 44 (12.1%) ADRs in the intervention group were assessed using the Karch and -Lasagna scale. The mean hospital expense was 21,949.12 ± 2,311.25 yuan in the control group and 17,566.25 ± 1,082.56 yuan in the intervention group. The mean drug cost was 6,250.69 ± 589.35 yuan and 4,894.22 ± 356.14 yuan (1 US$ = 6.37 yuan). The mean length of stay was 12.23 ± 2.51 days and 8.35 ± 1.32 days in the control and intervention groups, respectively. Patient satisfaction increased significantly. CONCLUSION: PCP reduced the length of stay for patients and drug-related adverse events, increased the rational use of drugs, cost-effectiveness, patient satisfaction, and consequently, improved the quality of service in surgery medicine.

Low-Cost Self-Calibration Data Glove Based on Space-Division Multiplexed Flexible Optical Fiber Sensor.

Wearable devices such as data gloves have experienced tremendous growth over the past two decades. It is vital to develop flexible sensors with fast response, high sensitivity and high stability for intelligent data gloves. Therefore, a tractable low-cost flexible data glove with self-calibration function based on a space-division multiplexed flexible optical fiber sensor is proposed. A simple, stable and economical method was used to fabricate flexible silicone rubber fiber for a stretchable double-layered coaxial cylinder. The test results show that the fiber is not sensitive to the temperature range of (20~50 °C) and exhibits excellent flexibility and high stability under tensile, bending and torsional deformation. In addition, the signal detection part of the data glove enables compact and efficient real-time information acquisition and processing. Combined with a self-calibration function that can improve the accuracy of data acquisition, the data glove can be self-adaptive according to different hand sizes and bending habits. In a gesture capture test, it can accurately recognize and capture each gesture, and guide the manipulator to make the same action. The low-cost, fast-responding and structurally robust data glove has potential applications in areas such as sign language recognition, telemedicine and human-robot interaction.

Does Health Crises Effect Tourism: Role of Financial Inclusion for Green Financial Development.

Tourism is impacted by all types of crises, no matter how big or small. Even though many studies have examined tourism crises, most focus on the number of tourists arriving and departing. As a result of this lack of information, The adaptive differences in tourist behavior caused by various crises are not well understood. When it comes to inbound tourism, the financial and health-related crisis can significantly impact the tourist profile of the country and its visitors' spending habits. The findings show that the health crisis has a significant positive impact on tourism. Moreover, COVID_deaths and COVID_confirm_cases decrease the international tourism in developed and developing countries. According to the study's findings, tourists' sensitivity to crises varies between short- and long-haul markets. The evidence shows that financial inclusion has a significant positive impact on various aspects of tourism development in China. Hence, this article offers numerous policy and practical suggestions for sustainable tourism management.

Green Credit Policy and Corporate Stock Price Crash Risk: Evidence From China.

Using the promulgation of Green Credit Guidelines in China as the research setting, this paper exploits a quasi-natural experiment to examine the impact of green credit policy on the stock price crash risk of heavy-polluting firms. The results show that green credit policy significantly increases the risk of stock price crash of heavy-polluting firms. Such impact is transmitted through increased financial constraints and reduced information transparency. In addition, we find that the impact of green credit policy on the stock price crash risk is more pronounced in firms with weak external governance and a small size. Our findings provide policy implications for mitigating corporate risks and promoting corporate sustainability.

Out-of-pocket medical expenditure and associated factors of advanced colorectal cancer in China: a multi-center cross-sectional study.

Background: Colorectal cancer (CRC) causes a substantial disease burden in China. Information on the medical expenditure of CRC patients is critical for decision-makers to allocate medical resources reasonably, however, relevant data is limited in China, especially advanced CRC. The aim of this survey was to quantify the out-of-pocket medical expenditure of advanced CRC and explore associated factors. Methods: A nation-wide, multi-center, cross-sectional survey was conducted from March 2020 to March 2021. Nineteen hospitals in seven geographical regions were selected by multi-stage stratified sampling. For each eligible CRC patient with stage III or IV disease in the selected hospitals, the socio-demographics, clinical information, and range of out-of-pocket medical expenditure data were collected based on patients' self-reporting or medical records. Multivariable logistic analysis was used to explore associated factors of medical expenditure. All statistical analyses were conducted using SAS 9.4. Results: The mean age of the 4,428 advanced CRC patients included was 59.5±11.6 years, 59.6% were male, and 80.1% of patients were in stage III or IV at the time of diagnosis. Besides, 57.2% of patients had an annual household income of less than 50,000 Chinese Yuan (CNY), 40.9% of patients had an out-of-pocket medical expenditure of 50,000-99,999 CNY. As for the affordability of medical expenditure, 33.2% could afford 50,000-99,999 CNY. Multivariate analysis showed that patients who were in the southern [odds ratio (OR): 1.63, 95% confidence interval (CI): 1.31-2.03] and southwestern (OR: 1.55, 95% CI: 1.25-1.93), were in stage III at the time of diagnosis (OR: 1.33, 95% CI: 1.13-1.57), visited three or more hospitals (OR: 1.26, 95% CI: 1.04-1.52), had sought cross-regional health care (OR: 1.60, 95% CI: 1.40-1.83), used genetic testing (OR: 1.26, 95% CI: 1.10-1.45) and targeted drugs (OR: 2.12, 95% CI: 1.79-2.51) had higher out-of-pocket medical expenditure. Conclusions: Patients with advanced CRC had a high out-of-pocket medical expenditure. It is necessary to strengthen the prevention and control of CRC to reduce the disease burden; also, it is critical to deepen the reform of the medical system, increase proportion of medical insurance reimbursement, and remove barriers to cross-regional health care.

A Practical Assessment of the Disinfectant Efficacy of UV Light with and without Ozone Using a Novel Transfer Hatch in a Research Animal Facility.

Most in vivo animal research and breeding using mice and rats in China takes place in facilities under barrier conditions. Items being moved across the barrier are typically disinfected using UV radiation in a transfer hatch. However, the time periods necessary for this disinfection technique are inefficient, and disinfection is frequently incomplete, especially if concealed surfaces are present. The current study used a newly developed transfer hatch incorporating both UV and ozone disinfection to examine disinfection efficacy against 4 bacteria species (Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Acinetobacter baumannii). Disinfection trials used UV and ozone, applied separately and in combination, for up to 30 min. Separate and combined treatments were also tested with a UV barrier. We found that if UV radiation has direct contact with surfaces, it is an efficient disinfection method. However, where surfaces are concealed by a UV barrier, UV radiation performs relatively poorly. The results of this study indicate that a combination of UV and ozone produces the most effective disinfection and is markedly quicker than current disinfection times for UV applied on its own. This novel transfer hatch design therefore allows more complete and efficient disinfection, improves workflow, and reduces barrier breaches by pathogens that may affect animal health and welfare and compromise research outcomes.