Sexual dysfunction in Wilson disease: Prevalence and influencing factors.

Wilson disease is a rare neurogenetic disorder that receives significant attention due to its manifestations, such as jaundice, cirrhosis, tremor, dystonia, and others. However, the impact of Wilson disease on sexual function has been overlooked. In this study, we aimed to investigate current status of sexual dysfunction in Wilson disease. In this study, we investigated the sexual function status and possible influencing factors of 245 Wilson disease patients by questionnaire. Our study identified sexual dysfunction as a prevalent issue in Wilson disease patients, with an overall prevalence of 49.0 %, of which 33.9 % in males and 63.7 % in females, both higher than the prevalence of sexual dysfunction in the normal Chinese population. Compared with non-sexual dysfunction patients, sexual dysfunction was more common in the older age group, females, less educated, rural residence, no occupation, lower income, taking sedatives/antipsychotics, and high SIS scores (P < 0.05). Our binary logistic regression analysis revealed that older age (OR: 1.103, 95 %CI: 1.058-1.151, P < 0.001), being female (OR: 5.900,95 %CI: 2.966-11.736, P < 0.001), and the use of antipsychotics or sedatives (OR: 3.277,95 %CI: 1.065-10.077, P < 0.05) were all positively linked with an increased risk of sexual dysfunction. Despite the well-known symptoms of Wilson disease, sexual dysfunction is also a frequent issue in Wilson disease patients, necessitating further attention.

Low-cost and simple optical system based on wavefront coding and deep learning.

With the development of computational imaging, the integration of optical system design and digital algorithms has made more imaging tasks easier to perform. Wavefront coding (WFC) is a typical computational imaging technique that is used to address the constraints of optical aperture and depth of field. In this paper, we demonstrated a low-cost and simple optical system based on WFC and deep learning. We constructed an optimized encoding method for the phase plate under the framework of deep learning, which reduces the requirement for aberration correction in the full field of view. Optical coding was achieved with just a double-bonded lens and a simple cubic phase mask, and digital decoding used the deep residual UNet++ network framework. The final image obtained has good resolution, whereas the depth of field of the system expanded by a factor of 13, which is of great significance for the high-precision inspection and attaching of small parts of machine vision.

Using Mobility Data to Understand and Forecast COVID19 Dynamics

Disease dynamics, human mobility, and public policies co-evolve during a pandemic such as COVID-19. Understanding dynamic human mobility changes and spatial interaction patterns are crucial for understanding and forecasting COVID-19 dynamics. We introduce a novel graph-based neural network(GNN) to incorporate global aggregated mobility flows for a better understanding of the impact of human mobility on COVID-19 dynamics as well as better forecasting of disease dynamics. We propose a recurrent message passing graph neural network that embeds spatio-temporal disease dynamics and human mobility dynamics for daily state-level new confirmed cases forecasting. This work represents one of the early papers on the use of GNNs to forecast COVID-19 incidence dynamics and our methods are competitive to existing methods. We show that the spatial and temporal dynamic mobility graph leveraged by the graph neural network enables better long-term forecasting performance compared to baselines.

Estimating the Changing Infection Rate of COVID-19 Using Bayesian Models of Mobility

In order to prepare for and control the continued spread of the COVID-19 pandemic while minimizing its economic impact, the world needs to be able to estimate and predict COVID-19s spread. Unfortunately, we cannot directly observe the prevalence or growth rate of COVID-19; these must be inferred using some kind of model. We propose a hierarchical Bayesian extension to the classic susceptible-exposed-infected-removed (SEIR) compartmental model that adds compartments to account for isolation and death and allows the infection rate to vary as a function of both mobility data collected from mobile phones and a latent time-varying factor that accounts for changes in behavior not captured by mobility data. Since confirmed-case data is unreliable, we infer the models parameters conditioned on deaths data. We replace the exponential-waiting-time assumption of classic compartmental models with Erlang distributions, which allows for a more realistic model of the long lag between exposure and death. The mobility data gives us a leading indicator that can quickly detect changes in the pandemics local growth rate and forecast changes in death rates weeks ahead of time. This is an analysis of observational data, so any causal interpretations of the models inferences should be treated as suggestive at best; nonetheless, the models inferred relationship between different kinds of trips and the infection rate do suggest some possible hypotheses about what kinds of activities might contribute most to COVID-19s spread.

Predicting Onset of COVID-19 with Mobility-Augmented SEIR Model

Timely interventions and early preparedness of healthcare resources are crucial measures to tackle the COVID-19 disease. To aid these efforts, we developed the Mobility-Augmented SEIR model (MA-SEIR) that leverages Googles aggregate and anonymized mobility data to augment classic compartmental models. We show in a retrospective analysis how this method can be applied at an early stage in the COVID-19 epidemic to forecast its subsequent spread and onset in different geographic regions, with minimal parameterization of the model. This provides insight into the role of near real-time aggregate mobility data in disease spread modeling by quantifying substantial changes in how populations move both locally and globally. These changes would be otherwise very hard to capture using less timely data.

Assessing the impact of coordinated COVID-19 exit strategies across Europe

As rates of new COVID-19 cases decline across Europe due to non-pharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. Here, we use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we found that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe meant half as many lockdown periods were required to end community transmission continent-wide. One Sentence SummaryEU coordination in easing restrictions is key to preventing resurgent COVID-19 outbreaks and stopping community transmission.

Interplay of global multi-scale human mobility, social distancing, government interventions, and COVID-19 dynamics

This work quantifies mobility changes observed during the different phases of the pandemic world-wide at multiple resolutions - county, state, country - using an anonymized aggregate mobility map that captures population flows between geographic cells of size 5 km2. As we overlay the global mobility map with epidemic incidence curves and dates of government interventions, we observe that as case counts rose, mobility fell and has since then seen a slow but steady increase in flows. Further, in order to understand mixing within a region, we propose a new metric to quantify the effect of social distancing on the basis of mobility.Taking two very different countries sampled from the global spectrum, We analyze in detail the mobility patterns of the United States (US) and India. We then carry out a counterfactual analysis of delaying the lockdown and show that a one week delay would have doubled the reported number of cases in the US and India. Finally, we quantify the effect of college students returning back to school for the fall semester on COVID-19 dynamics in the surrounding community. We employ the data from a recent university outbreak (reported on August 16, 2020) to infer possible Reff values and mobility flows combined with daily prevalence data and census data to obtain an estimate of new cases that might arrive on a college campus. We find that maintaining social distancing at existing levels would be effective in mitigating the extra seeding of cases. However, potential behavioral change and increased social interaction amongst students (30% increase in Reff) along with extra seeding can increase the number of cases by 20% over a period of one month in the encompassing county. To our knowledge, this work is the first to model in near real-time, the interplay of human mobility, epidemic dynamics and public policies across multiple spatial resolutions and at a global scale.

[Epidemiological study of hepatolenticular degeneration at Hanshan County, Anhui Province].

OBJECTIVE: To identify the incidence and morbidity rates of hepatolenticular degeneration (HLD) at Hanshan County, Anhui Province. METHODS: According to the principles of age stratification, cluster and random sampling, a total of 112 810 subjects were screened by cornea slit-lamp examination during the period of November 2008 to October 2009. The subjects were from recruited from schools, factories, communities, institutions and villages at Hanshan County. And they belonged to the age group of 7 - 75 years. At the same time, each subject was evaluated by the clinical examination with regards to the presence of such clinical manifestations as brain, liver, kidney, skin and other organ damage. And the examinations of copper biochemistry and abdominal ultrasound were performed for those subjects with K-F rings or their clinical manifestations suspicious of HLD. In order to confirm or exclude HLD, the penicillamine challenge test (PCT) was performed if necessary. RESULTS: Seven HLD patients had a definite diagnosis of HLD. There were classical Wilson type (n = 1), pseudosclerosis type (n = 1), mental disorder type (n = 1), liver type (n = 1) and presymptomatic (n = 3). The incidence rate was 2.66/100 000 and the prevalence rate 6.21/100 000 at Hanshan County, Anhui Province. CONCLUSION: HLD is a common disease. In order to avoid a misdiagnosis and prevent an incorrect treatment, physicians should pay more attention to this curable disease and try their best to achieve early detection, early diagnosis and early treatment.

[Effect of Gandou Decoction IV combined with short-term decoppering therapy with sodium dimercapto-sulphonate on serum indexes of hepatic fibrosis in patients with Wilson' s disease].

OBJECTIVE: To observe the effect of Gandou Decoction IV (GDIV) on serum indexes of hepatic fibrosis and liver function in patients with Wilson's disease (WD). METHODS: Sixty-one WD patients were assigned to two groups, 30 patients in the sodium dimercaptosulphonate (DMPS) group and 31 patients in the GD IV group. Both groups received 8 courses of DMPS treatment with 6 days as one course, and the GD IV group was given GD IV additionally. Serum indexes of liver function were examined, serum matrix metalloproteinase-1 (MMP-1) and tissue inhibitor of metalloproteinase-1 (TIMP-1) were detected by double antibody sandwish ABC enzyme-linked immunosorbent assay (ELISA), and serum hyaluronic (HA), laminin (LN), procollagen III (PC III) and collagen type IV (C-IV) were determined by radioimmunoassay (RIA). RESULTS: After treatment, all indexes of hepatic fibrosis and liver function had no significant change in the DMPS group, while in the GD IV group, the serum TIMP-1 level markedly decreased (P <0.05), the ratio of MMP-1/TIMP-1 significantly increased (P <0.01), and serum indexes of liver function markedly decreased (P < 0.05), but the changes in serum levels of HA, LN and PCIII, as well as in serum MMP-1 and C-IV were insignificant (P> 0.05), though they showed a trend of decreasing or increasing, respectively. CONCLUSION: Short-term decopper-ing treatment with DMPS alone has no significant effect on hepatic function and serum fibrosis indexes in WD patients, while combined with GD IV, it can improve liver function and display an anti-fibrosis effect through inhibiting the serum TIMP-1 level and increasing the ratio of MMP-1/TIMP-1.

Comparison of long lasting therapeutic effects between succimer and penicillamine on hepatolenticular degeneration.

AIM:To compare the long-term effect of succimer (Suc) with that of penicillamine (Pen) in treating hepatolenticular degeneration (HLD).METHODS:One hundred and twenty patients with HLD were divided into 2 groups. Group A (n =60) received Suc 750mg,po.bid.Group B (n =60) received Pen 250mg, po. qid. The period of maintenance treatment varied from 6 months to 3 years, averaging 1.5 years. Symptoms and therapeutic effects were evaluated by modified Goldstein scale.RESULTS:The total effectiveness of group A in two different periods of treatment were 80% and 85% respectively, higher than those of group B (58% and 59% respectively)(P < 0.05). Suc also had obvious curative effects for the patients who failed in the use of Pen. There were fewer side effect in group A than in group B (P < 0.05). Suc and Pen could increase urinary copper excretion effectively and continually.CONCLUSION:Suc is more effective and safer than Pen. Clinically, it can replace Pen as first-choice drug for long-term maintenance therapy of HLD.