XRRmeta: Exact Inference for Random-Effects Meta-Analyses with Rare Events (preprint)

Meta-analysis aggregates information across related studies to provide more reliable statistical inference and has been a vital tool for assessing the safety and efficacy of many high profile pharmaceutical products. A key challenge in conducting a meta-analysis is that the number of related studies is typically small. Applying classical methods that are asymptotic in the number of studies can compromise the validity of inference, particularly when heterogeneity across studies is present. Moreover, serious adverse events are often rare and can result in one or more studies with no events in at least one study arm. While it is common to use arbitrary continuity corrections or remove zero-event studies to stabilize or define effect estimates in such settings, these practices can invalidate subsequent inference. To address these significant practical issues, we introduce an exact inference method for comparing event rates in two treatment arms under a random effects framework, which we coin "XRRmeta". In contrast to existing methods, the coverage of the confidence interval from XRRmeta is guaranteed to be at or above the nominal level (up to Monte Carlo error) when the event rates, number of studies, and/or the within-study sample sizes are small. XRRmeta is also justified in its treatment of zero-event studies through a conditional inference argument. Importantly, our extensive numerical studies indicate that XRRmeta does not yield overly conservative inference. We apply our proposed method to reanalyze the occurrence of major adverse cardiovascular events among type II diabetics treated with rosiglitazone and in a more recent example examining the utility of face masks in preventing person-to-person transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease 2019 (COVID-19).

Low Dose Naltrexone use for the management of post acute sequelae of COVID 19 (preprint)

Post-Acute Sequelae of SARS-CoV-2 (PASC), also known as Long COVID, is globally estimated to have affected up to 40-50% of individuals who were infected with SARS-CoV-2. The causes of PASC are being investigated, and there are no established therapies. One of the leading hypotheses for the cause of PASC is the persistent activation of innate immune cells with increased systemic inflammation. Naltrexone is a medication with anti-inflammatory and immunomodulatory properties that has been used in other conditions that overlap with PASC. In this study we performed retrospective review of a clinical cohort of 59 patients at a single academic center who received low-dose naltrexone (LDN) off-label as a potential therapeutic intervention for PASC. The use of LDN was associated with improved clinical symptoms (fatigue, brain fog, post exertional malaise/PEM, unrefreshing sleep, sleep pattern, and headache), fewer number of symptoms, and better functional status. This observational finding warrants further testing in rigorous, randomized, placebo-controlled clinical trials.

Four thermostatic steps: A novel CRISPR-Cas12-based system for the rapid at-home detection of respiratory pathogens.

The outbreak of coronavirus disease 2019 (COVID-19) in 2019 has severely damaged the world's economy and public health and made people pay more attention to respiratory infectious diseases. However, traditional quantitative real-time polymerase chain reaction (qRT-PCR) nucleic acid detection kits require RNA extraction, reverse transcription, and amplification, as well as the support of large-scale equipment to enrich and purify nucleic acids and precise temperature control. Therefore, novel, fast, convenient, sensitive and specific detection methods are urgently being developed and moving to proof of concept test. In this study, we developed a new nucleic acid detection system, referred to as 4 Thermostatic steps (4TS), which innovatively allows all the detection processes to be completed in a constant temperature device, which performs extraction, amplification, cutting of targets, and detection within 40 min. The assay can specifically and sensitively detect five respiratory pathogens, namely SARS-CoV-2, Mycoplasma felis (MF), Chlamydia felis (CF), Feline calicivirus (FCV), and Feline herpes virus (FHV). In addition, a cost-effective and practical small-scale reaction device was designed and developed to maintain stable reaction conditions. The results of the detection of the five viruses show that the sensitivity of the system is greater than 94%, and specificity is 100%. The 4TS system does not require complex equipment, which makes it convenient and fast to operate, and allows immediate testing for suspected infectious agents at home or in small clinics. Therefore, the assay system has diagnostic value and significant potential for further reducing the cost of early screening of infectious diseases and expanding its application. KEY POINTS: • The 4TS system enables the accurate and specific detection of nucleic acid of pathogens at 37 °C in four simple steps, and the whole process only takes 40 min. •A simple alkali solution can be used to extract nucleic acid. • A small portable device simple to operate is developed for home diagnosis and detection of respiratory pathogens.

Enhanced inflammation and suppressed adaptive immunity in COVID-19 with prolonged RNA shedding.

Little is known regarding why a subset of COVID-19 patients exhibited prolonged positivity of SARS-CoV-2 infection. Here, we found that patients with long viral RNA course (LC) exhibited prolonged high-level IgG antibodies and higher regulatory T (Treg) cell counts compared to those with short viral RNA course (SC) in terms of viral load. Longitudinal proteomics and metabolomics analyses of the patient sera uncovered that prolonged viral RNA shedding was associated with inhibition of the liver X receptor/retinoid X receptor (LXR/RXR) pathway, substantial suppression of diverse metabolites, activation of the complement system, suppressed cell migration, and enhanced viral replication. Furthermore, a ten-molecule learning model was established which could potentially predict viral RNA shedding period. In summary, this study uncovered enhanced inflammation and suppressed adaptive immunity in COVID-19 patients with prolonged viral RNA shedding, and proposed a multi-omic classifier for viral RNA shedding prediction.

Multi-omics in COVID-19: Seeing the unseen but overlooked in the clinic.

COVID-19 is an ongoing pandemic of global concern and is unlikely to disappear. This commentary discusses how multi-omics technologies have helped uncover the molecular processes and dynamics underlying COVID-19 initiation, progression, and transmission, and how lack of standardization has limited their application in clinical settings.

Application of Remote Fetal Heart Rate Monitoring Based on Internet in Late Pregnancy During the COVID-19 Pandemic (preprint)

Objective：To evaluate the consistency of pregnancy outcomes and related clinical monitoring indicators in high and low risk group under two different monitoring methods during COVID-19 pandemic.Methods：760 cases of late pregnant women admitted to our hospital from March 2020 to February 2021 were selected for this study. They were randomly divided into control group (n = 380) and observation group (n = 380). Observation group the fetus was monitored remotely at home，while the control group went to the hospital for fetal heart monitoring.To evaluate the consistency of pregnancy outcomes and related clinical monitoring indicators in different groups under two different monitoring .Results:It was found that there was no statistically significant difference between the two groups in terms of pregnancy outcome (including mode of delivery, postpartum hemorrhage, etc.) and neonatal outcome (including preterm birth rate, neonatal weight, neonatal Apgar score, etc.) (p>0.05). It should also be noted that the difference in EPDS score results between the two groups was statistically significant (p<0.05) . Meanwhile, there was a statistically significant difference in the total cost of labor and delivery between two groups (p<0.05) and total time spent on labor and delivery between the two groups(p<0.05).Conclusions:Remote Fetal Heart Rate Monitoring Based on Internet is an innovative, acceptable, and effective reduced-frequency prenatal examination model. Compared to routine prenatal examination, Remote Fetal Heart Rate Monitoring Based on Internet resulted in higher patient satisfaction and lower prenatal stress. Besides, Remote Fetal Heart Rate Monitoring Based on Internet compared with the traditional fetal heart monitoring does not affect the pregnancy outcomes of pregnant women with different risk factors, maternal accept degree is high, and to reduce the medical resources pressure. It is an effective and feasible way for self-monitoring of pregnancies in late pregnancy during the pandemic of COVID-19 and is worthy of clinical application.

Mental Health and Related Factors of Adolescent Students During Coronavirus Disease 2019 (COVID-19) Pandemic.

OBJECTIVE: Adolescents are at a special stage of physical and mental development, which is a susceptible period for mental disorders. Since the outbreak of coronavirus pneumonia in December 2019, long term stress may have negative effects on the mental health of the adolescents. In the context of the coronavirus disease 2019 (COVID-19), the study was designed to investigate the mental and psychological health of adolescents in China and its possible related factors. METHODS: A cross-sectional study design was adopted using a structured questionnaire which were distributed through the Internet to measure depression, anxiety, life events and stress related factors. Descriptive statistics and multiple regression analyses were conducted to process the data. RESULTS: The final sample comprised 795 adolescents. The total detection rate of depression was 76.48% and the total detection rate of anxiety was 33.08%. ANOVA showed that there were significant differences in depression scores in terms of gender, anxiety scores, history of mental disorders, COVID-19 knowledge reserve, family and social contradictions (p<0.05). And there were significant differences in anxiety scores in terms of gender, depression scores, mental health knowledge reserves, family and social contradictions (p<0.05). Multiple regression analysis showed that anxiety score, health status and COVID-19 knowledge reserve were positively associated with depression score (p<0.01), and history of psychosocial disorders was negatively associated with depression score (p<0.05); depression score, family and social contradictions were significantly positively correlated with anxiety score (p<0.01), and history of mental disorders was significantly negatively correlated with SDS score (p<0.01). CONCLUSION: During the outbreak of COVID-19, adolescent students with better understanding of the pandemic, more complete knowledge of mental health, and better family and social relationship had less impact on their mental health. Therefore, to ensure a sound social support system, elaborate health instruction, and family communication and mutual understanding are conducive to alleviating the psychological stress caused by the epidemic, and it is positive for adolescent students to maintain a good mental health.

MPS-Net: Multi-Point Supervised Network for CT Image Segmentation of COVID-19.

The new coronavirus, which has become a global pandemic, has confirmed more than 88 million cases worldwide since the first case was recorded in December 2019, causing over 1.9 million deaths. Since COIVD-19 lesions have clear imaging features on CT images, it is suitable for the auxiliary diagnosis and treatment of COVID-19. Deep learning can be used to segment the lesions areas of COVID-19 in CT images to help monitor the epidemic situation. In this paper, we propose a multi-point supervision network (MPS-Net) for segmentation of COVID-19 lung infection CT image lesions to solve the problem of a variety of lesion shapes and areas. A multi-scale feature extraction structure, a sieve connection structure (SC), a multi-scale input structure and a multi-point supervised training structure were implemented into MPS-Net. In order to increase the ability to segment various lesion areas of different sizes, the multi-scale feature extraction structure and the sieve connection structure will use different sizes of receptive fields to extract feature maps of various scales. The multi-scale input structure is used to minimize the edge loss caused by the convolution process. In order to improve the accuracy of segmentation, we propose a multi-point supervision training structure to extract supervision signals from different up-sampling points on the network. Experimental results showed that the dice similarity coefficient (DSC), sensitivity, specificity and IOU of the segmentation results of our model are 0.8325, 0.8406, 09988 and 0.742, respectively. The experimental results demonstrated that the network proposed in this paper can effectively segment COVID-19 infection on CT images. It can be used to assist the diagnosis and treatment of new coronary pneumonia.

Chest radiograph-based artificial intelligence predictive model for mortality in community-acquired pneumonia.

BACKGROUND: Chest radiograph (CXR) is a basic diagnostic test in community-acquired pneumonia (CAP) with prognostic value. We developed a CXR-based artificial intelligence (AI) model (CAP AI predictive Engine: CAPE) and prospectively evaluated its discrimination for 30-day mortality. METHODS: Deep-learning model using convolutional neural network (CNN) was trained with a retrospective cohort of 2235 CXRs from 1966 unique adult patients admitted for CAP from 1 January 2019 to 31 December 2019. A single-centre prospective cohort between 11 May 2020 and 15 June 2020 was analysed for model performance. CAPE mortality risk score based on CNN analysis of the first CXR performed for CAP was used to determine the area under the receiver operating characteristic curve (AUC) for 30-day mortality. RESULTS: 315 inpatient episodes for CAP occurred, with 30-day mortality of 19.4% (n=61/315). Non-survivors were older than survivors (mean (SD)age, 80.4 (10.3) vs 69.2 (18.7)); more likely to have dementia (n=27/61 vs n=58/254) and malignancies (n=16/61 vs n=18/254); demonstrate higher serum C reactive protein (mean (SD), 109 mg/L (98.6) vs 59.3 mg/L (69.7)) and serum procalcitonin (mean (SD), 11.3 (27.8) µg/L vs 1.4 (5.9) µg/L). The AUC for CAPE mortality risk score for 30-day mortality was 0.79 (95% CI 0.73 to 0.85, p<0.001); Pneumonia Severity Index (PSI) 0.80 (95% CI 0.74 to 0.86, p<0.001); Confusion of new onset, blood Urea nitrogen, Respiratory rate, Blood pressure, 65 (CURB-65) score 0.76 (95% CI 0.70 to 0.81, p<0.001), respectively. CAPE combined with CURB-65 model has an AUC of 0.83 (95% CI 0.77 to 0.88, p<0.001). The best performing model was CAPE incorporated with PSI, with an AUC of 0.84 (95% CI 0.79 to 0.89, p<0.001). CONCLUSION: CXR-based CAPE mortality risk score was comparable to traditional pneumonia severity scores and improved its discrimination when combined.

Cytoplasmic tail truncation of SARS-CoV-2 Spike protein enhances titer of pseudotyped vectors but masks the effect of the D614G mutation (preprint)

The high pathogenicity of SARS-CoV-2 requires it to be handled under biosafety level 3 conditions. Consequently, Spike protein pseudotyped vectors are a useful tool to study viral entry and its inhibition, with retroviral, lentiviral (LV) and vesicular stomatitis virus (VSV) vectors the most commonly used systems. Methods to increase the titer of such vectors commonly include concentration by ultracentrifugation and truncation of the Spike protein cytoplasmic tail. However, limited studies have examined whether such a modification also impacts the proteins function. Here, we optimized concentration methods for SARS-CoV-2 Spike pseudotyped VSV vectors, finding that tangential flow filtration produced vectors with more consistent titers than ultracentrifugation. We also examined the impact of Spike tail truncation on transduction of various cell types and sensitivity to convalescent serum neutralization. We found that tail truncation increased Spike incorporation into both LV and VSV vectors and resulted in enhanced titers, but had no impact on sensitivity to convalescent serum inhibition. In addition, we analyzed the effect of the D614G mutation, which became a dominant SARS-CoV-2 variant early in the pandemic. Our studies revealed that, similar to the tail truncation, D614G independently increases Spike incorporation and vector titers, but that this effect is masked by also including the cytoplasmic tail truncation. Therefore, the use of full-length Spike protein, combined with tangential flow filtration, is recommended as a method to generate high titer pseudotyped vectors that retain native Spike protein functions.

Multi-organ proteomic landscape of COVID-19 autopsies.

The molecular pathology of multi-organ injuries in COVID-19 patients remains unclear, preventing effective therapeutics development. Here, we report a proteomic analysis of 144 autopsy samples from seven organs in 19 COVID-19 patients. We quantified 11,394 proteins in these samples, in which 5,336 were perturbed in the COVID-19 patients compared to controls. Our data showed that cathepsin L1, rather than ACE2, was significantly upregulated in the lung from the COVID-19 patients. Systemic hyperinflammation and dysregulation of glucose and fatty acid metabolism were detected in multiple organs. We also observed dysregulation of key factors involved in hypoxia, angiogenesis, blood coagulation, and fibrosis in multiple organs from the COVID-19 patients. Evidence for testicular injuries includes reduced Leydig cells, suppressed cholesterol biosynthesis, and sperm mobility. In summary, this study depicts a multi-organ proteomic landscape of COVID-19 autopsies that furthers our understanding of the biological basis of COVID-19 pathology.

Exact Inference for Disease Prevalence Based on a Test with Unknown Specificity and Sensitivity (preprint)

To make informative public policy decisions in battling the ongoing COVID-19 pandemic, it is important to know the disease prevalence in a population. There are two intertwined difficulties in estimating this prevalence based on testing results from a group of subjects. First, the test is prone to measurement error with unknown sensitivity and specificity. Second, the prevalence tends to be low at the initial stage of the pandemic and we may not be able to determine if a positive test result is a false positive due to the imperfect specificity of the test. The statistical inference based on large sample approximation or conventional bootstrap may not be sufficiently reliable and yield confidence intervals that do not cover the true prevalence at the nominal level. In this paper, we have proposed a set of 95% confidence intervals, whose validity is guaranteed and doesn't depend on the sample size in the unweighted setting. For the weighted setting, the proposed inference is equivalent to a class of hybrid bootstrap methods, whose performance is also more robust to the sample size than those based on asymptotic approximations. The methods are used to reanalyze data from a study investigating the antibody prevalence in Santa Clara county, California, which was the motivating example of this research, in addition to several other seroprevalence studies where authors had tried to correct their estimates for test performance. Extensive simulation studies have been conducted to examine the finite-sample performance of the proposed confidence intervals.

Risk of pre-term births and major birth defects resulting from paternal intake of COVID-19 medications prior to conception (preprint)

Objective: . With the ongoing COVID-19 pandemic, large numbers of people will receive one of the several medications proposed to treat COVID-19, including patients of reproductive age. Given that some medications have shown adverse effects on sperm quality, there might be a transgenerational concern. We aim at examining the association between drugs proposed to treat COVID-19 when taken by the father around conception and any pre-term birth or major birth defects in offspring in a nation-wide cohort study using Danish registry data. Offspring whose father filled at least one prescription of the following medications in the three months preceding conception were considered exposed: chloroquine, hydroxychloroquine, losartan, azithromycin, naproxen, dexamethasone and prednisone. Results: . For azithromycin and naproxen, large numbers of offspring were exposed (> 1800 offspring), and we found no association with adverse birth outcomes. For chloroquine, losartan and dexamethasone, exposure was intermediate (~900 offspring), and there was no statistically significant association with birth defects. For hydroxychloroquine and prednisone, exposure was limited (<300 offspring). Our evidence suggests that azithromycin and naproxen are safe with respect to pre-term birth and birth defects. For the other drugs investigated larger exposures are needed for conclusive statements.

Development of a lateral flow immunoassay strip for rapid detection of IgG antibody against SARS-CoV-2 virus.

The ongoing worldwide SARS-CoV-2 epidemic clearly has a tremendous influence on public health. Molecular detection based on oral swabs was used for confirmation of SARS-CoV-2 infection. However, high false negative rates were reported. We describe here the development of a point-of-care (POC) serological assay for the detection of IgG antibody against SARS-CoV-2. The principle of a lateral flow immunoassay strip (LFIAs) consists of fixing SARS-CoV-2 nucleocapsid protein to the surface of the strip and coupling anti-human IgG with colloidal gold nanoparticles (Au NPs). A series of parameters of this method were optimized, including the concentration of coating antigen, BSA blocking concentration and pH value for conjugation. The entire detection process took 15-20 min with a volume of 80 µL of the analyte solution containing 10 µL of serum and 70 µL sample diluent. The performance of the established assay was evaluated using serum samples of the clinically diagnosed cases of Coronavirus Disease 2019 (COVID-19). Our results indicated that the LFIAs for SARS-CoV-2 had satisfactory stability and reproducibility. As a result, our fast and easy LFIAs could provide a preliminary test result for physicians to make the correct diagnosis of SARS-CoV-2 infections along with alternative testing methods and clinical findings, as well as seroprevalence determination, especially in low-resource countries.

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Adjusting Coronavirus prevalence estimates for laboratory test kit error (preprint)

Testing representative populations to determine the prevalence or percent of the population with active SARS-Cov-2 (COVID-19) infection and/or antibodies to infection is being recommended as essential for making public policy decisions to open-up or to continue enforcing national, state and local government rules to shelter-in-place. However, all laboratory tests are imperfect and have estimates of sensitivity and specificity less than 100% - in some cases considerably less than 100%. That error will lead to biased prevalence estimates. If the true prevalence of COVID-19 is low, possibly in the range of 1-5%, then testing error will lead to a constant background of bias that will most likely be larger and possibly much larger than the true prevalence itself. As a result, what is needed is a method for adjusting prevalence estimates for testing error. In this paper we outline methods for adjusting prevalence estimates for testing error both prospectively in studies being planned and retrospectively in studies that have been conducted. The methods if employed would also help to harmonize study results within countries and around the world. Adjustment can lead to more accurate prevalence estimates and to better policy decisions.

Recurrence of SARS-CoV-2 PCR positivity in COVID-19 patients: a single center experience and potential implications (preprint)

IMPORTANCE How to appropriately care for patients who become PCR-negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still not known. Patients who have recovered from coronavirus disease 2019 (COVID-19) could profoundly impact the health care system if a subset were to be PCR-positive again with reactivated SARS-CoV-2. OBJECTIVE To characterize a single center COVID-19 cohort with and without recurrence of PCR positivity, and develop an algorithm to identify patients at high risk of retest positivity after discharge to inform health care policy and case management decision-making. DESIGN, SETTING, AND PARTICIPANTS A cohort of 414 patients with confirmed SARS-CoV-2 infection, at The Second Affiliated Hospital of Southern University of Science and Technology in Shenzhen, China from January 11 to April 23, 2020. EXPOSURES Polymerase chain reaction (PCR) and IgM-IgG antibody confirmed SARS-CoV-2 infection. MAIN OUTCOMES AND MEASURES Univariable and multivariable statistical analysis of the clinical, laboratory, radiologic image, medical treatment, and clinical course of admission/quarantine/readmission data to develop an algorithm to predict patients at risk of recurrence of PCR positivity. RESULTS 16.7% (95CI: 13.0%-20.3%) patients retest PCR positive 1 to 3 times after discharge, despite being in strict quarantine. The driving factors in the recurrence prediction model included: age, BMI; lowest levels of the blood laboratory tests during hospitalization for cholinesterase, fibrinogen, albumin, prealbumin, calcium, eGFR, creatinine; highest levels of the blood laboratory tests during hospitalization for total bilirubin, lactate dehydrogenase, alkaline phosphatase; the first test results during hospitalization for partial pressure of oxygen, white blood cell and lymphocyte counts, blood procalcitonin; and the first test episodic Ct value and the lowest Ct value of the nasopharyngeal swab RT PCR results. Area under the ROC curve is 0.786. CONCLUSIONS AND RELEVANCE This case series provides clinical characteristics of COVID-19 patients with recurrent PCR positivity, despite strict quarantine, at a 16.7% rate. Use of a recurrence prediction algorithm may identify patients at high risk of PCR retest positivity of SARS-CoV-2 and help modify COVID-19 case management and health policy approaches.

The effect of Arbidol Hydrochloride on reducing mortality of Covid-19 patients: a retrospective study of real world date from three hospitals in Wuhan (preprint)

BACKGROUND The worldwide COVID-19 pandemic develops rapidly. There is a pressing need to find an effective therapy. METHODS We have assembled a cohort consisting 504 hospitalized COVID-19. Information of patients characteristics and antiviral medication use during hospital stay is collected. The study objective is to evaluate the treatment efficacy of selected antiviral medications on mortality and lesion absorption based on chest CT scan. RESULTS The overall mortality rate was 15.67% in the cohort. Older age, lower SpO2 level, bigger lesion, early admission data, and the presence of pre-existing conditions were associated with higher mortality. After adjusting for sex, pre-existing condition, age, SpO2, lesion size, admission data, hospital, and anti-viral medications use, Arbidol and Oseltamivir use is associated with a reduction in mortality. The OR is 0.183 (95% CI, 0.075 to 0.446; p<0.001) for Arbidol and 0.220 (95% CI, 0.069 to 0.707; p=0.011) for Oseltamivir. Compared with patients taking neither Arbidol nor Oseltamivir, the OR is 0.253 (95% CI, 0.064 to 1.001; p=0.050) for patients taking Oseltamivir only; 0.190 (95% CI, 0.076 to 0.473; p<0.001) for patients taking Arbidol only; and 0.030 (95% CI, 0.003 to 0.310; p=0.003) for patients taking both, after adjusting for patients characteristics and Lopinavir/Ritonavir use. Similarly, Arbidol is also associated with faster lesion absorption after adjusting for patients characteristics as well as Oseltamivir and Lopinavir/Ritonavir use. CONCLUSIONS Arbidol is able to substantially associated with a reduction in mortality among hospitalized COVID-19 patients. The combination of Arbidol and Oselmativir may further associated with a reduction in mortality. There is no proven treatment benefit of Lopinavir/Ritonavir.