Vertical structures and microphysical characteristics of summer precipitation in North China detected by GPM-DPR.

The vertical structure and microphysical characteristics of stratiform precipitation (SP) and convective precipitation (CP) in North China are revealed based on the GPM-DPR product during boreal summer of 2014-2021 in this study. Additionally, the differences in precipitation features between the mountain and the plain are investigated. Under the combined influence of climatic factors and local topography, the precipitation amount is larger in the plain than in the mountain while precipitation frequency exhibits an opposite pattern. The proportions of the two precipitation types are similar in the mountain and the plain, with CP contributing to approximately a quarter of total precipitation frequency. In terms of mean intensity, both SP and CP are roughly 20 % more intense in the plain than in the mountain. The greater number of light SPs is a major contributor to higher precipitation frequency in the mountain, while more intense CPs result in larger precipitation amount in the plain. Compared to the mountain, the precipitation system is deeper in the plain, where higher storm top altitudes (STAs) and larger freezing level heights contribute to more intense CPs. Besides, it is observed that for the STA, more intense CPs occur in the plain compared to the mountain. In both the mountain and the plain, the coalescence process is dominant in the low-level layers for heavy (8-20 mm/h) to storm-level (>20 mm/h) CPs. Compared to the mountain, the low-level growth of hydrometeor size and radar reflectivity is more significant in the plain. These findings are important to quantitative precipitation estimation and precipitation prediction in the mountainous region, and can help understand the influence of local topography on precipitation.

Immunotoxicity of microplastics in fish.

Plastic waste degrades slowly in aquatic environments, transforming into microplastics (MPs) and nanoplastics (NPs), which are subsequently ingested by fish and other aquatic organisms, causing both physical blockages and chemical toxicity. The fish immune system serves as a crucial defense against viruses and pollutants present in water. It is imperative to comprehend the detrimental effects of MPs on the fish immune system and conduct further research on immunological assessments. In this paper, the immune response and immunotoxicity of MPs and its combination with environmental pollutants on fish were reviewed. MPs not only inflict physical harm on the natural defense barriers like fish gills and vital immune organs such as the liver and intestinal tract but also penetrate cells, disrupting intracellular signaling pathways, altering the levels of immune cytokines and gene expression, perturbing immune homeostasis, and ultimately compromising specific immunity. Initially, fish exposed to MPs recruit a significant number of macrophages and T cells while activating lysosomes. Over time, this exposure leads to apoptosis of immune cells, a decline in lysosomal degradation capacity, lysosomal activity, and complement levels. MPs possess a small specific surface area and can efficiently bind with heavy metals, organic pollutants, and viruses, enhancing immune responses. Hence, there is a need for comprehensive studies on the shape, size, additives released from MPs, along with their immunotoxic effects and mechanisms in conjunction with other pollutants and viruses. These studies aim to solidify existing knowledge and delineate future research directions concerning the immunotoxicity of MPs on fish, which has implications for human health.

Heterogeneous integration of an on-chip Nd:YAG whispering gallery mode laser with a lithium-niobate-on-insulator platform.

The integration of heterogeneous optical components onto an optical platform is crucial for the advancement of photonic chips. To achieve this, efficient coupling of optical signals between components and the platform is essential. Here, we have successfully integrated a Nd:YAG microdisk laser with a lithium-niobate-on-insulator (LNOI) photonic platform by modulating the propagation modes of LNOI. Ridge waveguides are fabricated on the LNOI by carefully adjusting the cross-sectional dimensions to enable the propagation of higher-order propagation modes. This ridge waveguide ensures that the effective refractive index of the higher-order mode closely matches that of the fundamental mode of the Nd:YAG microdisk, ensuring efficient waveguide-microdisk coupling. This on-chip laser, consisting of an Nd:YAG microdisk and LNOI integration, achieves a maximum output power of 23 µW, and a mode suppression ratio of 53.6 dB. This research presents an efficient approach for constructing highly functional heterogeneous integrated optical chips.

Impact of the early COVID-19 pandemic on adult mental health-related dispensed medications, hospitalizations and specialist outpatient visits in Norway and Sweden: Interrupted time series analysis.

AIMS: Norway and Sweden had different early pandemic responses that may have impacted mental health management. The aim was to assess the impact of the early COVID-19 pandemic on mental health-related care. METHODS: We used national registries in Norway and Sweden (1 January 2018-31 December 2020) to define 2 cohorts: (i) general adult population; and (ii) mental health adult population. Interrupted times series regression analyses evaluated step and slope changes compared to prepandemic levels for monthly rates of medications (antidepressants, antipsychotics, anxiolytics, hypnotics/sedatives, lithium, opioid analgesics, psychostimulants), hospitalizations (for anxiety, bipolar, depressive/mood, eating and schizophrenia/delusional disorders) and specialist outpatient visits. RESULTS: In Norway, immediate reductions occurred in the general population for medications (-12% antidepressants to -7% hypnotics/sedatives) except for antipsychotics; and hospitalizations (-33% anxiety disorders to -17% bipolar disorders). Increasing slope change occurred for all medications except psychostimulants (+1.1%/month hypnotics/sedatives to +1.7%/month antidepressants); and hospitalization for anxiety disorders (+5.5%/month), depressive/mood disorders (+1.7%/month) and schizophrenia/delusional disorders (+2%/month). In Sweden, immediate reductions occurred for antidepressants (-7%) and opioids (-10%) and depressive/mood disorder hospitalizations (-11%) only with increasing slope change in psychostimulant prescribing of (0.9%/month). In contrast to Norway, increasing slope changes occurred in specialist outpatient visits for depressive/mood disorders, eating disorders and schizophrenia/delusional disorders (+1.5, +1.9 and +2.3%/month, respectively). Similar changes occurred in the pre-existing mental health cohorts. CONCLUSION: Differences in early COVID-19 policy response may have contributed to differences in adult mental healthcare provision in Norway and Sweden.

PatchCL-AE: Anomaly detection for medical images using patch-wise contrastive learning-based auto-encoder.

Anomaly detection is an important yet challenging task in medical image analysis. Most anomaly detection methods are based on reconstruction, but the performance of reconstruction-based methods is limited due to over-reliance on pixel-level losses. To address the limitation, we propose a patch-wise contrastive learning-based auto-encoder for medical anomaly detection. The key contribution is the patch-wise contrastive learning loss that provides supervision on local semantics to enforce semantic consistency between corresponding input-output patches. Contrastive learning pulls corresponding patch pairs closer while pushing non-corresponding ones apart between input and output, enabling the model to learn local normal features better and improve discriminability on anomalous regions. Additionally, we design an anomaly score based on local semantic discrepancies to pinpoint abnormalities by comparing feature difference rather than pixel variations. Extensive experiments on three public datasets (i.e., brain MRI, retinal OCT, and chest X-ray) achieve state-of-the-art performance, with our method achieving over 99% AUC on retinal and brain images. Both the contrastive patch-wise supervision and patch-discrepancy score provide targeted advancements to overcome the weaknesses in existing approaches.

A Capture-Recapture-based Ascertainment Probability Weighting Method for Effect Estimation With Under-ascertained Outcomes.

Outcome under-ascertainment, characterized by the incomplete identification or reporting of cases, poses a substantial challenge in epidemiologic research. While capture-recapture methods can estimate unknown case numbers, their role in estimating exposure effects in observational studies is not well established. This paper presents an ascertainment probability weighting framework that integrates capture-recapture and propensity score weighting. We propose a nonparametric estimator of effects on binary outcomes that combines exposure propensity scores with data from two conditionally independent outcome measurements to simultaneously adjust for confounding and under-ascertainment. Demonstrating its practical application, we apply the method to estimate the relationship between health care work and coronavirus disease 2019 testing in a Swedish region. We find that ascertainment probability weighting greatly influences the estimated association compared to conventional inverse probability weighting, underscoring the importance of accounting for under-ascertainment in studies with limited outcome data coverage. We conclude with practical guidelines for the method's implementation, discussing its strengths, limitations, and suitable scenarios for application.

Biphase Pd Nanosheets with Atomic-Hybrid RhOx/Pd Amorphous Skins Disentangle the Activity-Stability Trade-Off in Oxygen Reduction Reaction.

The activity-stability trade-off relationship of oxygen reduction reaction (ORR) is a tricky issue that strikes the electrocatalyst population and hinders the widespread application of fuel cells. Here neoteric biphase Pd nanosheets that are structured with ultrathin two-dimensional crystalline Pd inner cores and ≈1 nm thin atomic-hybrid RhOx/Pd amorphous skins, named c/a-Pd@PdRh NSs, for disentangling this trade-off dilemma for alkaline ORR are developed. The superthin amorphous skins significantly amplify the quantity of flexibly low-coordinated atoms for electrocatalysis. An in situ selected oxidation of the top-surface Rh dopants creates atomically hybrid RhOx/Pd disorder surfaces. Detailed energy spectra and theoretical simulation confirm that these RhOx/Pd interfaces can arouse a surface charge redistribution, causing significant electron deficiency and lowered d-band center for surface Pd. Meanwhile, anticorrosive Rh/RhOx species can thermodynamically passivate the neighboring Pd atoms from oxidative dissolution. Thanks to these amplified interfacial effects, the biphase c/a-Pd@PdRh NSs simultaneously exhibit a superhigh ORR activity (5.92 A mg-1, 22.8 times that of Pt/C) and an outstanding long-lasting stability after 100k cycles of accelerated durability test, showcasing unprecedented electrocatalysts for breaking the activity-stability trade-off relationship of ORR. This work paves a bran-new strategy for designing high-performance electrocatalysts through creating modulated amorphous skins on low-dimensional nanomaterials.

Survival of Critically Ill COVID-19 Patients in Sweden During the First Two and a Half Years of the Pandemic.

OBJECTIVES: Some studies have examined survival trends among critically ill COVID-19 patients, but most were case reports, small cohorts, and had relatively short follow-up periods. We aimed to examine the survival trend among critically ill COVID-19 patients during the first two and a half years of the pandemic and investigate potential predictors across different variants of concern periods. DESIGN: Prospective cohort study. SETTING: Swedish ICUs, between March 6, 2020, and December 31, 2022. PATIENTS: Adult COVID-19 ICU patients of 18 years old or older from the Swedish Intensive Care Register (SIR) that were linked to multiple other national registers. MEASUREMENT AND MAIN RESULTS: Survival probability and predictors of COVID-19 death were estimated using Kaplan-Meier and Cox regression analysis. Of 8975 patients, 2927 (32.6%) died. The survival rate among COVID-19 critically ill patients appears to have changed over time, with a worse survival in the Omicron period overall. The adjusted hazard ratios (aHRs) comparing older and younger ages were consistently strong but slightly attenuated in the Omicron period. After adjustment, the aHR of death was significantly higher for men, older age (40+ yr), low income, and with comorbid chronic heart disease, chronic lung disease, impaired immune disease, chronic renal disease, stroke, and cancer, and for those requiring invasive or noninvasive respiratory supports, who developed septic shock or had organ failures (p < 0.05). In contrast, foreign-born patients, those with booster vaccine, and those who had taken steroids had better survival (aHR = 0.87; 95% CI, 0.80-0.95; 0.74, 0.65-0.84, and 0.91, 0.84-0.98, respectively). Observed associations were similar across different variant periods. CONCLUSIONS: In this nationwide Swedish cohort covering over two and a half years of the pandemic, ICU survival rates changed over time. Older age was a strong predictor across all periods. Furthermore, most other mortality predictors remained consistent across different variant periods.

Impact of chronic oral glucocorticoid treatment on mortality in patients with COVID-19: analysis of a population-based cohort.

OBJECTIVES: While glucocorticoid (GC) treatment initiated for COVID-19 reduces mortality, it is unclear whether GC treatment prior to COVID-19 affects mortality. Long-term GC use raises infection and thromboembolic risks. We investigated if patients with oral GC use prior to COVID-19 had increased mortality overall and by selected causes. DESIGN: Population-based observational cohort study. SETTINGS: Population-based register data in Sweden. PARTICIPANTS: All patients infected with COVID-19 in Sweden from January 2020 to November 2021 (n=1 200 153). OUTCOME MEASURES: Any prior oral GC use was defined as ≥1 GC prescription during 12 months before index. High exposure was defined as ≥2 GC prescriptions with a cumulative prednisolone dose ≥750 mg or equivalent during 6 months before index. GC users were compared with COVID-19 patients who had not received GCs within 12 months before index. We used Cox proportional hazard models and 1:2 propensity score matching to estimate HRs and 95% CIs, controlling for the same confounders in all analyses. RESULTS: 3378 deaths occurred in subjects with any prior GC exposure (n=48 806; 6.9%) and 14 850 among non-exposed (n=1 151 347; 1.3%). Both high (HR 1.98, 95% CI 1.87 to 2.09) and any exposure (1.58, 1.52 to 1.65) to GCs were associated with overall death. Deaths from pulmonary embolism, sepsis and COVID-19 were associated with high GC exposure and, similarly but weaker, with any exposure. High exposure to GCs was associated with increased deaths caused by stroke and myocardial infarction. CONCLUSION: Patients on oral GC treatment prior to COVID-19 have increased mortality, particularly from pulmonary embolism, sepsis and COVID-19.

Anomaly Detection for Medical Images Using Heterogeneous Auto-Encoder.

Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping between the input and output, which leads to failure in detecting abnormal samples; 2) the reconstruction considers the pixel-wise differences which may lead to an undesirable result. To mitigate the above problems, we propose a novel heterogeneous Auto-Encoder (Hetero-AE) for medical anomaly detection. Our model utilizes a convolutional neural network (CNN) as the encoder and a hybrid CNN-Transformer network as the decoder. The heterogeneous structure enables the model to learn the intrinsic information of normal data and enlarge the difference on abnormal samples. To fully exploit the effectiveness of Transformer in the hybrid network, a multi-scale sparse Transformer block is proposed to trade off modelling long-range feature dependencies and high computational costs. Moreover, the multi-stage feature comparison is introduced to reduce the noise of pixel-wise comparison. Extensive experiments on four public datasets (i.e., retinal OCT, chest X-ray, brain MRI, and COVID-19) verify the effectiveness of our method on different imaging modalities for anomaly detection. Additionally, our method can accurately detect tumors in brain MRI and lesions in retinal OCT with interpretable heatmaps to locate lesion areas, assisting clinicians in diagnosing abnormalities efficiently.

Antibiotic Use in Late Preterm and Full-Term Newborns.

Importance: Antibiotic treatment saves lives in newborns with early-onset sepsis (EOS), but unwarranted antibiotic use is associated with resistant bacteria and adverse outcomes later in life. Surveillance is needed to optimize treatment strategies. Objective: To describe antibiotic use in association with the incidence and mortality from EOS among late-preterm and full-term newborns. Design, Setting, and Participants: The Sweden Neonatal Antibiotic Use study was a nationwide observational study that included all late-preterm and full-term neonates born from January 1, 2012, to December 31, 2020, in neonatal units of all levels. All hospital live births from 34 weeks' gestation during the study period were included in the study. Data were collected from the Swedish Neonatal Quality Register and the Swedish Medical Birth Register. Data were analyzed from August 2022 to May 2023. Exposure: Admission for neonatal intensive care during the first week of life. Main Outcomes and Measures: The main outcomes were the usage of intravenous antibiotics during the first week of life, the duration of antibiotic therapy, the rate of culture-proven EOS, and mortality associated with EOS. Results: A total of 1 025 515 newborns were included in the study; 19 286 neonates (1.88%; 7686 girls [39.9%]; median [IQR] gestational age, 40 [38-41] weeks; median [IQR] birth weight, 3610 [3140-4030] g) received antibiotics during the first week of life, of whom 647 (3.4%) had EOS. The median (IQR) duration of antibiotic treatment in newborns without EOS was 5 (3-7) days, and there were 113 antibiotic-days per 1000 live births. During the study period there was no significant change in the exposure to neonatal antibiotics or antibiotic-days per 1000 live births. The incidence of EOS was 0.63 per 1000 live births, with a significant decrease from 0.74 in 2012 to 0.34 in 2020. Mortality associated with EOS was 1.39% (9 of 647 newborns) and did not change significantly over time. For each newborn with EOS, antibiotic treatment was initiated in 29 newborns and 173 antibiotic-days were dispensed. Conclusions and Relevance: This large nationwide study found that a relatively low exposure to antibiotics is not associated with an increased risk of EOS or associated mortality. Still, future efforts to reduce unwarranted neonatal antibiotic use are needed.

COVID-19 Outcomes and Vaccinations in Swedish Solid Organ Transplant Recipients 2020-2021: A Nationwide Multi-Register Comparative Cohort Study.

Increased COVID-19-related morbidity and mortality have been reported in solid organ transplant recipients (SOTRs). Most studies are underpowered for rigorous matching. We report infections, hospitalization, ICU care, mortality from COVID-19, and pertinent vaccination data in Swedish SOTRs 2020-2021. We conducted a nationwide cohort study, encompassing all Swedish residents. SOTRs were identified with ICD-10 codes and immunosuppressant prescriptions. Comparison cohorts were weighted based on a propensity score built from potential confounders (age, sex, comorbidities, socioeconomic factors, and geography), which achieved a good balance between SOTRs and non-SOTR groups. We included 10,372,033 individuals, including 9073 SOTRs. Of the SARS-CoV-2 infected, 47.3% of SOTRs and 19% of weighted comparator individuals were hospitalized. ICU care was given to 8% of infected SOTRs and 2% of weighted comparators. The case fatality rate was 7.7% in SOTRs, 6.2% in the weighted comparison cohort, and 1.3% in the unweighted comparison cohort. SOTRs had an increased risk of contracting COVID-19 (HR = 1.15 p < 0.001), being hospitalized (HR = 2.89 p < 0.001), receiving ICU care (HR = 4.59 p < 0.001), and dying (HR = 1.42 p < 0.001). SOTRs had much higher morbidity and mortality than the general population during 2020-2021. Also compared with weighted comparators, SOTRs had an increased risk of contracting COVID-19, being hospitalized, receiving ICU care, and dying. In Sweden, SOTRs were vaccinated earlier than weighted comparators. Lung transplant recipients had the worst outcomes. Excess mortality among SOTRs was concentrated in the second half of 2021.

Drug utilisation in children and adolescents before and after the start of the COVID-19 pandemic: Interrupted time-series analyses in three European countries.

BACKGROUND: The COVID-19 pandemic has affected children and adolescents in several ways, including worsened mental health, improvement of asthma, and increases in diabetes ketoacidosis. Less is known about how medication use in children and adolescents has been affected by the pandemic. OBJECTIVES: To explore how the COVID-19 pandemic affected drug utilisation in children and adolescents in Norway, Sweden, and Italy, by child age. METHODS: We conducted a longitudinal drug utilisation study among all children and adolescents (<18 years old) in Norway and Sweden and a nationwide paediatric database covering 3% of the paediatric population in Italy. We conducted an interrupted time-series analysis from January 2018 to December 2021, with March 2020 as the interruption point. Dispensing or prescription rates of antidepressants, anxiolytics, sleep medications, attention-deficit/hyperactivity disorder (ADHD) medications, insulin, and asthma medications were examined. RESULTS: The study population in January 2018 consisted of 3,455,521 children and adolescents (136,188 from Italy, 1,160,431 from Norway, and 2,158,902 from Sweden). For sleep medications and insulin, there were only minor changes in level or trend in some age groups after March 2020. For asthma medications, the pandemic was associated with an immediate decrease in dispensing in Norway and Sweden (range of change in level: -19.2 to -3.7 dispensings per 1000 person-months), and an increasing trend in all countries afterward (range of change in trend: 0.3-6.4 dispensings per 1000 person-months), especially for the youngest age groups. Among adolescents, the pandemic was associated with an increased trend for ADHD medications, antidepressants, and anxiolytics in Norway and Sweden, but not in Italy. CONCLUSIONS: The increasing trend of psychotropic medication dispensing, especially among adolescents after the start of the pandemic, is concerning and should be investigated further. Aside from a temporary effect on asthma medication dispensing, the pandemic did not greatly affect the dispensing of the medications investigated.

Atomically Dispersed Zn/Co-N-C as ORR Electrocatalysts for Alkaline Fuel Cells.

Hydrogen fuel cells have drawn increasing attention as one of the most promising next-generation power sources for future automotive transportation. Developing efficient, durable, and low-cost electrocatalysts, to accelerate the sluggish oxygen reduction reaction (ORR) kinetics, is urgently needed to advance fuel cell technologies. Herein, we report on metal-organic frameworks-derived nonprecious dual metal single-atom catalysts (SACs) (Zn/Co-N-C), consisting of Co-N4 and Zn-N4 local structures. These catalysts exhibited superior ORR activity with a half-wave potential (E1/2) of 0.938 V versus RHE (reversible hydrogen electrode) and robust stability (ΔE1/2 = -8.5 mV) after 50k electrochemical cycles. Moreover, this remarkable performance was validated under realistic fuel cell working conditions, achieving a record-high peak power density of ∼1 W cm-2 among the reported SACs for alkaline fuel cells. Operando X-ray absorption spectroscopy was conducted to identify the active sites and reveal catalytic mechanistic insights. The results indicated that the Co atom in the Co-N4 structure was the main catalytically active center, where one axial oxygenated species binds to form an Oads-Co-N4 moiety during the ORR. In addition, theoretical studies, based on a potential-dependent microkinetic model and core-level shift calculations, showed good agreement with the experimental results and provided insights into the bonding of oxygen species on Co-N4 centers during the ORR. This work provides a comprehensive mechanistic understanding of the active sites in the Zn/Co-N-C catalysts and will pave the way for the future design and advancement of high-performance single-site electrocatalysts for fuel cells and other energy applications.

Adaptive enhancement of cataractous retinal images for contrast standardization.

Cataract affects the quality of fundus images, especially the contrast, due to lens opacity. In this paper, we propose a scheme to enhance different cataractous retinal images to the same contrast as normal images, which can automatically choose the suitable enhancement model based on cataract grading. A multi-level cataract dataset is constructed via the degradation model with quantified contrast. Then, an adaptive enhancement strategy is introduced to choose among three enhancement networks based on a blurriness classifier. The blurriness grading loss is proposed in the enhancement models to further constrain the contrast of the enhanced images. During test, the well-trained blurriness classifier can assist in the selection of enhancement networks with specific enhancement ability. Our method performs the best on the synthetic paired data on PSNR, SSIM, and FSIM and has the best PIQE and FID on 406 clinical fundus images. There is a 7.78% improvement for our method compared with the second on the introduced [Formula: see text] score without over-enhancement according to [Formula: see text], which demonstrates that the proper enhancement by our method is close to the high-quality images. The visual evaluation on multiple clinical datasets also shows the applicability of our method for different blurriness. The proposed method can benefit clinical diagnosis and improve the performance of computer-aided algorithms such as vessel tracking and vessel segmentation.

The sociodemographic patterning of sick leave and determinants of longer sick leave after mild and severe COVID-19: a nationwide register-based study in Sweden.

BACKGROUND: Studies on sociodemographic differences in sick leave after coronavirus disease 2019 (COVID-19) are limited and research on COVID-19 long-term health consequences has mainly addressed hospitalized individuals. The aim of this study was to investigate the social patterning of sick leave and determinants of longer sick leave after COVID-19 among mild and severe cases. METHODS: The study population, from the Swedish multi-register observational study SCIFI-PEARL, included individuals aged 18-64 years in the Swedish population, gainfully employed, with a first positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 1 January 2020 until 31 August 2021 (n = 661 780). Using logistic regression models, analyses were adjusted for sociodemographic factors, vaccination, prior sick leave, comorbidities and stratified by hospitalization. RESULTS: In total, 37 420 (5.7%) individuals were on sick leave due to COVID-19 in connection with their first positive COVID-19 test. Individuals on sick leave were more often women, older, had lower income and/or were born outside Sweden. These differences were similar across COVID-19 pandemic phases. The highest proportion of sick leave was seen in the oldest age group (10.3%) with an odds ratio of 4.32 (95% confidence interval 4.18-4.47) compared with the youngest individuals. Among individuals hospitalized due to COVID-19, the sociodemographic pattern was less pronounced, and in some models, even reversed. The intersectional analysis revealed considerable variability in sick leave between sociodemographic groups (range: 1.5-17.0%). CONCLUSION: In the entire Swedish population of gainfully employed individuals, our findings demonstrated evident sociodemographic differences in sick leave due to COVID-19. In the hospitalized group, the social patterning was different and less pronounced.

Encoder-Decoder Contrast for Unsupervised Anomaly Detection in Medical Images.

Unsupervised anomaly detection (UAD) aims to recognize anomalous images based on the training set that contains only normal images. In medical image analysis, UAD benefits from leveraging the easily obtained normal (healthy) images, avoiding the costly collecting and labeling of anomalous (unhealthy) images. Most advanced UAD methods rely on frozen encoder networks pre-trained using ImageNet for extracting feature representations. However, the features extracted from the frozen encoders that are borrowed from natural image domains coincide little with the features required in the target medical image domain. Moreover, optimizing encoders usually causes pattern collapse in UAD. In this paper, we propose a novel UAD method, namely Encoder-Decoder Contrast (EDC), which optimizes the entire network to reduce biases towards pre-trained image domain and orient the network in the target medical domain. We start from feature reconstruction approach that detects anomalies from reconstruction errors. Essentially, a contrastive learning paradigm is introduced to tackle the problem of pattern collapsing while optimizing the encoder and the reconstruction decoder simultaneously. In addition, to prevent instability and further improve performances, we propose to bring globality into the contrastive objective function. Extensive experiments are conducted across four medical image modalities including optical coherence tomography, color fundus image, brain MRI, and skin lesion image, where our method outperforms all current state-of-the-art UAD methods. Code is available at: https://github.com/guojiajeremy/EDC.

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study.

Aims: To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway. Method: We employed linked data from the national health registries of Sweden and Norway to conduct our study. We focused on individuals in Sweden with confirmed SARS-CoV-2 infection through RT-PCR testing up to August 2022 as our study cohort. Within this group, we identified hospitalized cases as those who were admitted to the hospital within 14 days of testing positive for SARS-CoV-2 and matched them with five controls from the same cohort who were not hospitalized due to SARS-CoV-2. Additionally, we identified individuals who died within 30 days after being hospitalized for COVID-19. To develop our disease risk scores, we considered various factors, including demographics, infectious, somatic, and mental health conditions, recorded diagnoses, and pharmacological treatments. We also conducted age-specific analyses and assessed model performance through 5-fold cross-validation. Finally, we performed external validation using data from the Norwegian population with COVID-19 up to December 2021. Results: During the study period, a total of 124,560 individuals in Sweden were hospitalized, and 15,877 individuals died within 30 days following COVID-19 hospitalization. Disease risk scores for both hospitalization and mortality demonstrated predictive capabilities with ROC-AUC values of 0.70 and 0.72, respectively, across the entire study period. Notably, these scores exhibited a positive correlation with the likelihood of hospitalization or death. In the external validation using data from the Norwegian COVID-19 population (consisting of 53,744 individuals), the disease risk score predicted hospitalization with an AUC of 0.47 and death with an AUC of 0.74. Conclusion: The disease risk score showed moderately good performance to predict COVID-19-related mortality but performed poorly in predicting hospitalization when externally validated.

Covid-19 vaccine effectiveness against post-covid-19 condition among 589 722 individuals in Sweden: population based cohort study.

OBJECTIVE: To investigate the effectiveness of primary covid-19 vaccination (first two doses and first booster dose within the recommended schedule) against post-covid-19 condition (PCC). DESIGN: Population based cohort study. SETTING: Swedish Covid-19 Investigation for Future Insights-a Population Epidemiology Approach using Register Linkage (SCIFI-PEARL) project, a register based cohort study in Sweden. PARTICIPANTS: All adults (≥18 years) with covid-19 first registered between 27 December 2020 and 9 February 2022 (n=589 722) in the two largest regions of Sweden. Individuals were followed from a first infection until death, emigration, vaccination, reinfection, a PCC diagnosis (ICD-10 diagnosis code U09.9), or end of follow-up (30 November 2022), whichever came first. Individuals who had received at least one dose of a covid-19 vaccine before infection were considered vaccinated. MAIN OUTCOME MEASURE: The primary outcome was a clinical diagnosis of PCC. Vaccine effectiveness against PCC was estimated using Cox regressions adjusted for age, sex, comorbidities (diabetes and cardiovascular, respiratory, and psychiatric disease), number of healthcare contacts during 2019, socioeconomic factors, and dominant virus variant at time of infection. RESULTS: Of 299 692 vaccinated individuals with covid-19, 1201 (0.4%) had a diagnosis of PCC during follow-up, compared with 4118 (1.4%) of 290 030 unvaccinated individuals. Covid-19 vaccination with any number of doses before infection was associated with a reduced risk of PCC (adjusted hazard ratio 0.42, 95% confidence interval 0.38 to 0.46), with a vaccine effectiveness of 58%. Of the vaccinated individuals, 21 111 received one dose only, 205 650 received two doses, and 72 931 received three or more doses. Vaccine effectiveness against PCC for one dose, two doses, and three or more doses was 21%, 59%, and 73%, respectively. CONCLUSIONS: The results of this study suggest a strong association between covid-19 vaccination before infection and reduced risk of receiving a diagnosis of PCC. The findings highlight the importance of primary vaccination against covid-19 to reduce the population burden of PCC.

Occupational noise exposure, noise annoyance, hearing-related symptoms, and emotional exhaustion - a participatory-based intervention study in preschool and obstetrics care.

A participatory-based intervention was performed in Sweden, aimed at improving the sound environment in one preschool (n = 20) and one obstetric ward (n = 50), with two controls each (n = 28, n = 66). Measured sound levels, and surveys of noise annoyance, hearing-related symptoms and emotional exhaustion were collected before, and three and nine months after the interventions, comparing intervention and control groups over time. The results of this first implementation in a limited number of workplaces showed significantly worsening of hyperacusis, sound-induced auditory fatigue, emotional exhaustion and increased sound levels in the preschool, and worsening of noise annoyance in both intervention groups. Increased risk awareness, limited implementation support and lack of psychosocial interventions may explain the worsening in outcomes, as might the worse baseline in the intervention groups. The complexity of the demands in human-service workplaces calls for further intervention studies.