After adjusting for age, we found no significant correlation between urinary antigen positivity and variations in the WHO ordinal scale and laboratory markers at admission and after 14 days. We found that a positive PUA result was not frequent and had no impact on clinical outcomes or clinical improvement. Our results did not support the routine use of PUA tests to select COVID-19 patients who will benefit from antibiotic therapy.Despite low rates of bacterial co-infections, most COVID-19 patients receive antibiotic therapy. We hypothesized that patients with positive pneumococcal urinary antigens (PUAs) would benefit from antibiotic therapy in terms of clinical outcomes . The San Matteo COVID-19 Registry (SMACORE) prospectively enrolls patients admitted for COVID-19 pneumonia at IRCCS Policlinico San Matteo, Pavia. We retrospectively extracted the data of patients tested for PUA from October to December 2020. Demographic, clinical, and laboratory data were recorded. Of 469 patients, 42 tested positive for PUA (8.95%), while 427 (91.05%) tested negative. A positive PUA result had no significant impact on death (HR 0.53 CI [0.22–1.28]  Staphylococcus aureus and Streptococcus pneumoniae were the most frequent co-pathogens [Bacterial co-infection is frequently described in patients affected by viral respiratory infections and is characterized by increased morbidity and mortality. During the 2009 H1A1 influenza pandemic, bacterial co-infection was reported in 12% of hospitalized patients, with the proportion growing to 30% in those admitted to intensive care units (ICU). athogens ,5,6,7. IS. pneumoniae is one of the most frequent co-pathogens in respiratory viral infections, and a PUA test is a non-invasive and cost-effective tool, with proven efficacy in the diagnosis of community-acquired pneumonia (CAP) [Despite these low rates of co-infections, the majority of COVID-19 patients (more than 70%) receive empirical antibiotic therapy for community-acquired pneumonia . In ordeia (CAP) ,11.We hypothesized that COVID-19 patients with a positive PUA result may benefit from appropriate antibiotic treatment in terms of mortality, ICU admission, and length of stay in comparison to those who tested negative. Furthermore, we analyzed clinical improvement between patients with positive PUA results and those with negative PUA results.The San Matteo COVID-19 Registry (SMACORE) cohort has collected records on COVID-19 patients hospitalized in our hospital  since the beginning of the pandemic (February 2020). Confirmed COVID-19 pneumonia was defined as radiological findings of pneumonia plus SARS-CoV-2 positivity on a nasopharyngeal swab analyzed with real-time polymerase chain reaction (RT-PCR) by our Molecular Virology Unit. Data on demographics, clinic, biochemical and microbiologic analyses, treatment, and outcome  are included. Data collection is still ongoing.Ethical approval for observational research using SMACORE data was obtained from the local ethics committee.In this study, we retrospectively extracted medical records from the SMACORE cohort for patients admitted between October 2020 and December 2020 and abstracted anonymized data from standardized data collection forms. This period of recruitment was chosen because a new test to detect urinary antigens was implemented in our laboratory at the beginning of October 2020. Demographic data included age and sex; clinical data included medical history, the Charlson comorbidity index (CCI), the onset of symptoms, steroid and antibiotic therapy prior to hospitalization, antibiotic therapy during hospitalization, and clinical status expressed with the WHO ordinal scale; laboratory findings included C-reactive protein levels, procalcitonin levels, and the neutrophil and lymphocyte count at admission and day 7 ,13. RadiWe divided our cohort of COVID-19 patients into two groups according to the PUA test result, and then compared them to assess the differences in primary and secondary outcomes.The primary outcome of the study was the 30-day in-hospital mortality rate. Secondary outcomes were ICU admission and clinical improvement according to the WHO ordinal scale at the 7th and 14th day after admission.The urine samples were collected and processed in our microbiology laboratory.S. pneumoniae Ag FIA , a fluorescence immunoassay. The test line of the assay is coated with rabbit anti S. pneumoniae capsular polysaccharides (CPS). The patient’s sample is applied to the test device, where it migrates through a membrane. If PUA is present, it will react with europium conjugated monoclonal anti-S. pneumoniae CPS in the conjugation pad and form antigen–antibody fluorescence particle complexes. The complexes move along the membrane to be captured by the rabbit anti S. pneumoniae CPS on the test line and form a fluorescence signal. The analyzer using pre-programmed algorithms scans the intensity of the fluorescence light. The analyzer indicates a Cutoff Index value (COI). A COI ≥ 1 is considered as a positive result. The test has shown a sensitivity of 80.8% and a specificity of 84.8% in patients aged >17 with cultural evidence of S. pneumoniae infection.PUAs were detected with STANDARD TM F t-tests for continuous variables.Data for continuous variables are presented as means and standard deviations. Data for categorical variables are presented as frequencies and percentages. Comparisons between positive PUA results and negative PUA results were performed using chi-square tests for categorical variables and The incidence, mechanisms, and patterns of missing data were then explored. Since the amount of missing data was non-negligible (higher than 10% in multiple variables) and followed a Missing at Random Mechanism, multiple imputations with predictive mean matching  were employed to reduce their impact. The quality of imputed data was ascertained by comparing the distributions of imputed datasets with the ones with complete data. Multiple imputed datasets were then employed to perform the subsequent analyses.First, two multivariable Cox proportional-hazard regression models were used to assess the predictive effect of a positive PUA result on 30-day mortality and ICU admission. Each multivariable model was developed including variables that were associated with the dependent variable in bivariate Cox regressions.Second, the predictive effect of a positive PUA result on length of stay (LOS) in patients who survived after the 30th day was investigated. To achieve this, a multivariable linear regression was used. Similarly, the multivariable model included the demographic, clinical, radiological, and laboratory variables which were significantly associated with LOS in bivariate analyses.Finally, linear mixed models were performed to evaluate possible differences between positive and negative PUA patients in the variance of biochemical values  and in the WHO ordinal scale . Time, PUA, and their interaction were included as fixed effects. A significant time–PUA interaction indicates that there was a difference between the two groups in the variance of biochemical values and the WHO ordinal scale. The fixed effect of age was also included in the models to account for confounding.Results of the Cox proportional-hazard regressions are reported as Hazard Ratios (HR) with 95% Confidence Intervals (CI); results of the linear regression and the linear mixed models are reported as unstandardized betas. The significance threshold was set at α = 0.05.The main characteristics of the 469 patients included in our cohort were as follows: 320 were male (68.2%) and 149 were female (31.8%), with a median age of 67.6 years (SD +/−14.7). Forty-two (8.95%) patients tested positive for PUA, while 427 (91.05%) tested negative. The characteristics of the study population are shown in Age, CCI, severe immunosuppression, days since the onset of symptoms, the WHO ordinal scale at admission, and CRP levels were statistically significant in predicting 30-day mortality in bivariate Cox regression models and were included in the multivariable model. A positive PUA result was not significantly associated with 30-day mortality in either the bivariate or the multivariable Cox regression models . RegardiThe procalcitonin levels at admission, the presence of both GGOs and FOs in CT scans, the WHO ordinal scale at admission, and antibiotic treatment with three different antibiotics during hospitalization were significant in predicting length of stay (LOS) in the bivariate analysis and were therefore included in the multivariate linear regression model. A positive PUA result did not show any predictive value for LOS in the bivariate analysis and multivariate linear regression .Patients with positive PUA results did not show a different variation on the WHO ordinal scale after 7 and 14 days compared with patients with negative PUA results in linear mixed models . After bThe appropriateness of antibiotic use is an important topic in the context of COVID-19 management in the era of antibiotic stewardship programs. In this study, we retrospectively analyzed the impact of PUA positivity on clinical outcomes in a cohort of COVID-19 patients. We did not observe any impact of a positive PUA result on clinical improvement or clinical outcomes in terms of mortality, ICU admission, and length of stay. Our results were similar to Desai et al.’s work, which showed no impact on mortality and length of stay in COVID-19 patients with a positive PUA result in the emergency department setting .Streptococcus pneumoniae co-infection in COVID-19 patients [Streptococcus pneumoniae and Legionella pneumophila is recommended only in patients who meet the criteria for severe CAP [Compared to influenza pandemics, our findings show a significantly lower rate of patients . This repatients ,7,14. Popatients ,16,17,18vere CAP . PUA hasvere CAP ,22. Morevere CAP .Our work has several limitations. First, due to the retrospective nature of this study, missing data were frequent, but this was accounted for in the analyses. Second, since we only enrolled patients in our center over a small interval of time, we had a limited sample. Furthermore, we did not investigate the presence of other bacterial co-infections through cultures from blood and respiratory samples, which may have helped us exclude cases of false positives due to test hypersensitivity. Regarding blood cultures, the sample collection was heterogenous from unit to unit, therefore the possible results were difficult to interpret. A sputum culture was not routinely performed, since COVID-19 patients usually present with a non-productive cough. Furthermore, aerosol-generating procedures such as sputum induction increase the risk of SARS-CoV-2 transmission and were therefore limited to selected cases. Further studies can provide evidence concerning the impact that this approach could have on clinical outcomes and antimicrobial consumption in COVID-19.


The infection of coronavirus disease (COVID-19) seriously threatens human life. It is urgent to generate effective and safe specific antibodies (Abs) against the pathogenic elements of COVID-19. Mice were immunized with SARS-CoV-2 spike protein antigens: S ectodomain-1  mixed in Alum adjuvant for 2 times and boosted with CoV weekly for 6 times. A portion of mice were treated with Maotai liquor  or/and heat stress (HS) together with CoV boosting. We observed that the anti-CoV Ab was successfully induced in mice that received the CoV/Alum immunization for 2 times. However, upon boosting with CoV, the CoV Ab production diminished progressively; spleen CoV Ab-producing plasma cell counts reduced, in which substantial CoV-specific Ab-producing plasma cells (sPC) were apoptotic. Apparent oxidative stress signs were observed in sPCs; the results were reproduced by exposing sPCs to CoV in the culture. The presence of MTL or/and HS prevented the CoV-induced oxidative stress in sPCs and promoted and stabilized the CoV Ab production in mice in re-exposure to CoV. In summary, CoV/Alum immunization can successfully induce CoV Ab production in mice that declines upon reexposure to CoV. Concurrent administration of MTL/HS stabilizes and promotes the CoV Ab production in mice. Since the beginning of 2020, COVID-19 infection has been a global pandemic disease that seriously threatens human life and has caused a catastrophic consequence for healthcare systems worldwide . The. TheE. cCOVID-19 seriously threatens human life. It is urgent to develop effective and safe remedies to be used in the prevention and treatment of COVID-19 infection. Our research group also participated in this important work to induce anti-SARS-CoV-2 Ab with a murine model. In line with published data , 4, we iThe present study showed that immunizing mice with CoV and Alum adjuvant successfully induced anti-CoV Ab in mice. This is supported by two datasets. One is that the experimental results are processed by ELISA, indicating that the induced Ab can bind CoV, one of the components of the SARS-CoV-2 virus; others also used this antigen to generate specific Ab against SARS-CoV-2 . AnotherWe observed that the CoV-specific Ab production declined in mice one week after the first two times of immunization. Similar phenomena were also found by others; e.g., Zhang et al. reported that the generated specific Ab against SARS-CoV F69 strain decreased significantly one week after the peak value . The prein vitro cell culture experiments, we found that exposure to CoV in the culture-induced sPC apoptosis. The data show that apoptosis initiating molecules, including CASP8, CASP7, CASP3, Fas, and FasL, were markedly upregulated in sPCs by CoV exposure. These apoptosis-related molecules are associated with the mitochondrial apoptosis pathway [By  pathway , suggest pathway . By the By modulating the ACE2/JAK1/STAT1 signal pathway, we successfully manipulated the CoV exposure-induced sPC apoptosis, increase in ROS/MDA, and decrease in SOD in sPCs. This suggests that CoV exposure induced the sPC apoptosis is regulatable. Published data indicate that MTL and HS can upregulate SOD expression , 12. By It should be mentioned that, in the present study, mice were immunized with CoV/Alum 2 times and boosting with CoV 6 times, while in current human COVID-19 vaccination, only two times are required in general. It seems that the 6-time boosting is unnecessary. However, it revealed an important factor that somehow mimics reexposure to COVID-19 virus after immunization. The results suggest that after receiving COVID-19 vaccination, sPCs were induced in the body. The sPCs may become apoptosis upon reexposure to COVID-19. The present data suggest that such a phenomenon can be prevented by relevant remedies, such as MTL and HS, as tested in the present study.In summary, the present study found that CoV immunization could induce CoV Ab production in mice, but the Ab production declined upon reexposure to CoV. The underlying mechanism of this phenomenon was CoV exposure-induced oxidative stress in sPCs and induced sPC apoptosis that could be prevented by employing the MTL supplement and HS.


Background and Objectives: Neutrophil-to-lymphocyte ratio (NLR), a very low cost, widely available marker of systemic inflammation, has been proposed as a potential predictor of short-term outcome in patients with intracerebral hemorrhage (ICH). Methods: Patients with ICH admitted to the Neurology Department during a two-year period were screened for inclusion. Based on eligibility criteria, 201 patients were included in the present analysis. Clinical, imaging, and laboratory characteristics were collected in a prespecified manner. Logistic regression models and receiver operating characteristics (ROC) curves were used to assess the performance of NLR assessed at admission (admission NLR) and 72 h later (three-day NLR) in predicting in-hospital death. Results: The median age of the study population was 70 years (IQR: 61–79), median admission NIHSS was 16 (IQR: 6–24), and median hematoma volume was 13.7 mL (IQR: 4.6–35.2 mL). Ninety patients (44.8%) died during hospitalization, and for 35 patients (17.4%) death occurred during the first three days. Several common predictors were significantly associated with in-hospital mortality in univariate analysis, including NLR assessed at admission . However, in multivariate analysis admission, NLR was not an independent predictor of in-hospital mortality . The subgroup analysis of 112 patients who survived the first 72 h of hospitalization showed that three-day NLR  and age  were the only independent predictors of in-hospital mortality. ROC curve analysis yielded an optimal cut-off value of three-day NLR for the prediction of in-hospital mortality of ≥6.3  and Kaplan–Meier analysis proved that ICH patients with three-day NLR ≥6.3 had significantly higher odds of in-hospital death . Conclusion: NLR assessed 72 h after admission is an independent predictor of in-hospital mortality in ICH patients and could be widely used in clinical practice to identify the patients at high risk of in-hospital death. Further studies to confirm this finding are needed. Intracerebral hemorrhage (ICH) is the second leading cause of stroke worldwide and accounts for roughly one-fourth of all strokes. It is estimated that ICH causes nearly 50% of all stroke-related deaths ,2. Many All components of the ICH score  are well-known independent prognostic factors of short-term mortality . RecentlNeuroinflammation produced by the initial brain insult in ICH impacts outcome, and inflammatory markers are thought to have a crucial role in secondary brain injury in ICH patients . NeutropAll patients with intracerebral hemorrhage admitted to the Neurology Department of the University Emergency Hospital Bucharest between July 2018 and July 2020 were screened for inclusion in this retrospective observational cohort study. We defined the following exclusion criteria for our initial cohort: (1) lack of signed informed consent forms; (2) admission likelihood of survival beyond three days considered very low; (3) initial admission in the ICU; (4) subsequent surgery or shunt placement; (5) inter-hospital transfers. For this study, the following additional exclusion criteria were used: (6) hemorrhage from a colocalized tumor; (7) traumatic ICH; (8) ischemic stroke with hemorrhagic transformation or aneurysmal SAH with parenchymal hematoma; (9) associated hematologic disorder; (10) associated malignancy; (11) missing follow-up data; (12) presence of an apparent infection on arrival; (13) steroid treatment; (14) time interval between ICH onset and first CT scan >24 h; (15) complete blood count (CBC) at admission not available. If the exact time of stroke onset was not available, the time when the subject was last seen normal was substituted only if it was <24 h. Demographic and clinical characteristics were collected from the medical records and electronic health system. The attending neurologist determined Glasgow Coma Scale (GCS) and NIHSS  scores on admission. In-hospital death from any cause was used as an outcome measure. Traditional risk factors definitions were detailed in a previously published protocol for our cohort . AdmissiCT scans performed at admission and during hospitalization were exported from the hospital picture archiving and communication system and imported in Analyze 14 . CT scans were analyzed blinded to date and clinical outcomes. All hematoma measurements were made by R.A.R. Hematoma volume (HV) was measured by manually segmenting each slice with an automatic trace function with threshold values between 44 and 80 HU. Artifacts were excluded if needed with the help of the draw limit function. Further image analysis was performed with Horos (Horosproject.org) by R.A.R. two weeks after finishing the initial volume measurements. Hematoma location was defined using The Cerebral Hemorrhage Anatomical Rating Instrument (CHARTS). For this study, lobar and deep hemorrhages were considered together with the uncertain origin category . Presencp-value ≤ 0.1 in univariate analysis. Subsequently, a predictive modelling strategy with the backward stepwise method of entering data was used. The goodness-of-fit of the logistic regression models was assessed with the Hosmer–Lemeshow test. Receiver operating characteristics (ROC) curve analysis was used to analyze the performance of 3-day NLR in predicting in-hospital death, and the Youden index to establish the optimal cut-off value for prediction of in-hospital death. Survival analysis was performed using Kaplan–Meier method, and the log-rank test was used to statistically compare the curves between the predefined groups of patients. A preset significance level of p < 0.05 was considered for all comparisons.All statistical analyses were performed using NCSS 12 Statistical Software  and MedCalc 18.11.3 Statistical Software . Continuous variables are described as median and 25–75 IQR, and categorical variables are presented as absolute numbers and percentages. For comparisons between continuous variables, we used Mann–Whitney or Kruskal–Wallis tests, according to the number of selected groups analyzed. For testing the strength of association between categorical variables, we used the chi-squared test or Fisher’s exact test, depending on the number of analyzed cases. Multivariate analysis was performed using logistic regression and taking into account full models that included variables achieving a A total of 383 patients were admitted to the Department of Neurology with ICH diagnosis during the study period. One hundred and fifty-six patients were excluded from the study based on established eligibility criteria: 43 patients were admitted more than 24 h after symptoms onset, 7 patients were transferred from other hospitals, 21 patients did not have signed informed-consent forms, 18 patients underwent a subsequent neurosurgery intervention, 26 were directly admitted to the ICU, 21 patients had incomplete admission blood analyses in the hospital records, 24 patients had missing follow-up data, and 22 patients were excluded due to other exclusion criteria. As a result, the final study population included 201 patients. p = 0.001) and more frequently diagnosed with diabetes mellitus  or coronary artery disease . All clinical severity scores showed a strong association with ICH mortality: median ICH score , median NIHSS score , and median GCS score . ICH patients who died during hospitalization were also more likely to have higher admission HV , and ventricular effraction was more frequently seen in this group . Admission blood glucose levels were significantly higher for patients who died during hospitalization . In univariate analysis, the following complete blood count parameters were associated with in-hospital death: leukocytes , neutrophils , admission NLR , and three-day NLR . p-value of ≤0.1 in univariate analysis were used to fit a full model of logistic regression analysis, with the following exceptions: (1) NIHSS score was included as sole clinical severity score and NLR as a pooled measure of complete blood count markers, to reduce redundancy; (2) dyslipidemia and hemoglobin (Hb) values were not included due to the questionable clinical significance of the results; (3) ICH score was not included due to potential collinearity with its component parameters. As a result, the final model included the following variables: age, admission NIHSS score, coronary artery disease, ICH volume at admission, GRAEB score, GFR rate, admission blood glucose, NLR at admission. p-value and chi-squared in Hosmer–Lemeshow goodness-of-fit test were 0.9 and 3.1, suggesting that the model fitted reasonably well. The results are summarized in Variables achieving a p = 0.3). Subsequently, a predictive modeling strategy was used (backward stepwise regression), which confirmed the performance of a model including age, admission NIHSS score, admission HV, and blood glucose levels ≥ 180 mg/dL in predicting in-hospital death.Factors increasing the odds of in-hospital death were age , admission NIHSS score , admission ICH volume , and admission blood glucose levels ≥ 180 mg/dL . NLR at admission lost statistical significance following multivariate analysis , presence of diabetes mellitus , coronary artery disease , admission ICH score , admission NIHSS score , admission GCS , admission HV , presence of ventricular effraction , admission GRAEB score , admission blood glucose >180 mg/dL , leukocytes count , neutrophils count , admission NLR , and three-day NLR . Detailed characteristics of this study population are presented in To establish the value of NLR assessed 72 h after admission (three-day NLR) for predicting in-hospital death, we reanalyzed the initial study population and selected the patients who survived the first 72 h of hospitalization and had a CBC analysis determined 72 h ± 6 h after the initial CBC. As 35 patients (17.4%) died during the first three days of hospitalization and 54 patients (26.8%) did not have a CBC at this time point, the study population included in this analysis consisted of 112 patients. Of these patients, 76 (67.9%) were alive at three days. In univariate analysis, clinical and imaging factors that remained significant predictors of in-hospital mortality for these patients were age  and three-day NLR  remained independent predictors for in-hospital death in this population. The results are summarized in As in-hospital death was observed for 36 patients in the population of patients surviving the first 72 h of hospitalization, we performed multivariate statistics using a full model that only included the following independent variables: age, admission NIHSS score, admission HV, and three-day NLR. Age . Ref.  This facSeveral findings of our study warrant a short discussion. First, in univariate analysis, dyslipidemia was significantly more frequent among our ICH patients discharged alive. In many previous studies and meta-analyses, low serum low-density cholesterol (LDLc) levels were shown to be associated with an increased risk of ICH and a higher risk of hematoma expansion and mortality ,24, whicThe current study has several limitations. First, the small sample size and its retrospective design prohibit the delineation of any potentially causal relationships. Second, some patients admitted with the diagnosis of ICH in our hospital were not included in our analyses, as the study was restricted to patients hospitalized in the Stroke Unit or Neurology Ward, missing those directly admitted in the ICU and those who underwent emergency neurosurgical procedures, including extraventricular drain placement. Third, CBC analysis determined 72 ± 6 h after the initial CBC was not available for 54 patients who survived the first 72 h of hospitalization, and therefore these patients were not included in the specific subgroup analysis, leading to a potential selection bias. However, the results of our study and their similarity with other reported findings on the same topic suggest that NLR assessed during the first few hours after ICH onset cannot be used as a predictor of in-hospital mortality while NLR assessed 72 h later is an independent predictor of in-hospital death in ICH patients and might be useful in clinical practice. The Kaplan–Meier curves showed an overall low survival in patients with three-day NLR values ≥6.3 mg/dL, and this cut-off point is in line with previously reported NLR values associated with a low probability of 30-day survival and higher odds of poor outcome .NLR assessed 72 h after admission is an independent predictor of in-hospital mortality in ICH patients and could be widely used in clinical practice as an additional biomarker aimed to identify the patients at high risk of in-hospital death. Further studies are required to determine whether specific interventions in patients with high NLR values at different time points after ICH onset can modify the short-term outcomes of this devastating disease.


TP53 gene , also known as the Li–Fraumeni syndrome, have a very high (70–100%) lifetime risk of developing cancer and at younger ages. Carriers are also prone to develop secondary tumours due to irradiation. Current guidelines recommend surveillance programmes within studies and the use of non-irradiation modalities such as whole-body MRI (WB-MRI). In 2016, the Swedish TP53 study (SWEP53) started inclusion, offering a surveillance program including WB-MRI. With this study, we aimed to describe the rate, anatomical distribution of malignant, indeterminate, and benign imaging findings as well as the associated further workup generated by the baseline WB-MRI in adult study participants. Our study identified the need of further workup in 19/61 participants, of whom three patients had a new cancer. WB-MRI appears to be a valuable surveillance strategy in families with hTP53rc syndrome. Individuals who are born with a disease-causing variant of the TP53-related cancer syndrome (hTP53rc), commonly referred to as the Li–Fraumeni syndrome (LFS), is studied in a prospective observational nationwide multi-centre study in Sweden (SWEP53). The aim of this sub-study is to evaluate whole-body MRI (WB-MRI) regarding the rate of malignant, indeterminate, and benign imaging findings and the associated further workup generated by the baseline examination. Individuals with hTP53rc were enrolled in a surveillance program including annual whole-body MRI (WB-MRI), brain-MRI, and in female carriers, dedicated breast MRI. A total of 68 adults ≥18 years old have been enrolled to date. Of these, 61 fulfilled the inclusion criteria for the baseline MRI scan. In total, 42 showed a normal scan, while 19 (31%) needed further workup, of whom three individuals (3/19 = 16%) were diagnosed with asymptomatic malignant tumours . Forty-three participants were women, of whom 21 had performed risk-reducing mastectomy prior to inclusion. The remaining were monitored with breast MRI, and no breast tumours were detected on baseline MRI. WB-MRI has the potential to identify asymptomatic tumours in individuals with hTP53rc syndrome. The challenge is to adequately and efficiently investigate all indeterminate findings. Thus, a multidisciplinary team should be considered in surveillance programs for individuals with hTP53rc syndrome.A surveillance strategy of the heritable  TP53 variant have a lifetime risk of 70–100% [TP53-related cancer syndrome (hTP53rc) rather than LFS [Carriers of a disease-causing germline  70–100% . During TP53rc are at risk of developing cancer at considerably younger ages (median age 25 years) than non-carriers, and up to 50% of all carriers develop a tumour before the age of 30 [TP53-carriers are more prone to develop multiple primary cancers [Individuals with hge of 30 ,5. Breasge of 30 . TP53-ca cancers . The pot cancers ,9,10 whiTP53rc would likely benefit from surveillance to facilitate early detection of tumours in a potentially curative stage. In 2016, Villani et al. [Persons with hi et al.  publisheTP53 variant, reported a 7% detection rate of new, localized primary cancers [TP53 variants. To our knowledge, there are as yet no other ongoing studies in the Scandinavian countries, but studies with WB-MRI are reported in Australia [A previous meta-analysis of baseline WB-MRI as a cancer screening tool, including 578 carriers of a pathogenic  cancers . The rea cancers  makes WBustralia , Brazil,ustralia , Great Bustralia , the Netustralia , France ustralia , and theustralia ,19. So fTP53 study (SWEP53), which aims to improve the clinical care of families with hTP53rc syndrome. The details of the SWEP53 study protocol have previously been described [The reported participants have been enrolled for WB-MRI within the Swedish escribed .The aim of this report is to evaluate baseline WB-MRI regarding the distribution and rate of radiological findings in terms of malignant, indeterminate, and benign lesions and the associated further workup generated by this surveillance strategy. TP53 variants were invited to participate in SWEP53 from 1 April 2016. Those who had performed their baseline WB-MRI by 1 May 2021 were included in this study. Inclusion was performed at the six cancer genetic units in Sweden, i.e., at the University Hospital in Umeå, Uppsala University Hospital, Karolinska University Hospital (Stockholm), Linköping University Hospital, Sahlgrenska University Hospital (Gothenburg), and Skåne University Hospital (Lund). Individuals were identified as carriers of a disease-causing TP53 variant by one of these four testing procedures: (1) through gene panel testing within the clinical workup of participants with a suspected hereditary breast cancer, (2) through gene panel testing within a national research study aiming at identifying novel high risk genes for breast cancer, (3) targeted testing of TP53 due to multiple primary cancers or family history fulfilling the Chompret 2015 criteria [TP53 variant previously identified in the family. Written informed consent was obtained prior to inclusion. Ethical permission was obtained by the regional ethical review board in Stockholm with approval number 2015/1600-3 with the amendments 2017/1527-32 and 2018/1690-32. Exclusion criteria were individuals with general contraindications to MRI, any co-morbidity that precludes treatment of a cancer found in the surveillance program, or an ongoing intensive cancer treatment, where priority was given to clinical examinations.Individuals with pathogenic or likely pathogenic germline criteria , or (4) The WB-MRI was defined as the baseline if it was either the first scan performed after study inclusion or, in some cases, a clinically performed WB-MRI prior to the inclusion date. The surveillance program included WB-MRI, a separate brain MRI, and in women with no previous bilateral risk-reducing mastectomy (RRM), breast MRI and breast ultrasound. All study participants were informed of the risk of possible secondary workup due to findings detected by the radiological and/or clinical examinations. The yearly WB-MRI examinations were intended to cover the whole body from the skull base to the feet . The SWEThe study sites were provided with a protocol , and we The evaluation of findings on whole-body MRI were single- or double read by radiologists and depending on need, local resources, and routines also separately evaluated by a neuro- and breast radiologist. For the purpose of reporting the findings on baseline WB-MRI, benign lesions were defined as those that were judged to be obviously benign on the MRI (and/or on prior imaging) requiring no further additional work-up. The clearly benign findings were handled as they would have been treated in a clinical setting. Findings referred to as indeterminate were those that required a further investigation to state whether benign or malignant. Malignant findings were defined as a new primary tumour, previously undiagnosed recurrence, or metastatic disease. Multiple lesions within the same organ (such as multiple liver metastases) were regarded as one finding.Clinical data were collected from the medical records, including information on family history of cancer, previous cancer diagnosis and age at onset, findings at WB-MRI, and any subsequent workup. A typical workflow was inclusion at the site by an oncologist or a clinical geneticist. Physical evaluation and referral for WB-MRI followed, and results of the imaging were managed by the referring physician in accordance with established clinical procedures. The radiological reports and results of the workups from each participating site were then reviewed by the central site at Karolinska University Hospital in Stockholm. All data presented are observational. Data are presented as numbers and proportions. The mean age for the whole study population was 39 years (range 18–74 years old). Out of the 61 individuals, 32 had a previous history of tumour diagnosis prior to study inclusion . Breast cancer was the most common (23 participants with 27 breast cancer diagnoses). A total of 18 individuals had had 21 other tumour types. Tumour onset ranged from 4 months to 71 years of age. Thirteen participants had previously had more than one different type of malignancy by the time of study inclusion .By 1 May 2021, a total of 68 adult participants had been included nationally in SWEP53, of whom 61 adults had performed their baseline scans . A total of 30 new lesions  were ideOverall, 9 of the 30 lesions (30%) that required further workup were malignant. All nine were identified in three individuals (3/61 = 5%): one participant had a new primary cancer, another had disseminated disease, and the third had a recurrent cancer. All patients were asymptomatic at the time of inclusion. Of the 21 indeterminate findings , Table 2In all 61 individuals, a total of 58 benign imaging findings were found, defined as benign by the radiologist without any need for further workup. The most common benign findings after further work-up were in the brain  and in the liver . TP53 variants showed that one-third of carriers had indeterminate imaging findings on baseline WB-MRI requiring further workup. Of these, all except one resulted in benign results. Of note, the rates of both indeterminate and benign imaging findings were higher than malignant findings, which were, in total, 9/88 lesions (10%) identified in 3/61 (5%) of all participants. A meta-analysis by Ballinger et al. in 2017 [TP53 variants from 13 different cohorts who underwent baseline WB-MRI, reported 7% new malignancies. In contrast with our study, Ballinger et al. only included new localised cancers as new malignancies, whereas we defined previously unknown metastatic cancers and recurrences as malignant findings. We found new, non-metastatic malignancies in 1/61 (1.6%), thus lower than Ballinger et al. This is likely not due to age differences, as the mean age in our study group was 39 years  and 41 years  compared to Ballinger et al., who reported a mean age of 33 years. However, our cohort was smaller (61 compared to 578), and thus, the estimate should be interpreted with caution. We also included both persons with a classical LFS tumour spectrum and individuals from families with predominantly hereditary breast cancer. This could possibly have led to a lower detection rate due to an overall lower tumour risk for cancers other than breast cancer. In addition, we did not detect any cases of breast cancer in the 22 women who had not undergone RRM. This is not surprising, as the women were previously offered annual breast surveillance with breast MRI and ultrasound according to national guidelines. The fact that one patient had disseminated disease but a normal clinical examination highlights the difficulties in detecting cancer solely through clinical check-ups. We had a detection rate of 31% of indeterminate imaging findings requiring further investigation. In the meta-analysis by Ballinger et al. [The results from this cohort of individuals with germline disease causing  in 2017 , comprisr et al. , the “far et al. . TP53 alterations. Instead of viewing the study participants as healthy individuals, perhaps the radiologists should consider them “patients with an unknown cancer” to minimize the risk of disregarding or overlooking potential malignant findings. Although we had a defined study protocol for WB-MRI, some local adaptations were made due to different technical platforms, which led to slightly different anatomical coverage and imaging protocols. Since both soft-tissue sarcomas and osteosarcomas often occur in the limbs ,23, it iTP53rc individuals with, for example, predominantly hereditary breast cancer from WB-MRI surveillance, and thereby reduce the risk of unnecessary workup. However, the benefits of repeated WB-MRI and risk stratification, potentially based on genotype–phenotype characterisations, is still under evaluation. Therefore, we suggest that all germline TP53 carriers should be surveilled with WB-MRI in accordance with the ERN GENTURIS guidelines independent of family history [In the future, it may be reasonable to exclude hTP53-related cancer syndrome may detect asymptomatic cancers. Benign and indeterminate imaging findings that require further work-up are more common than cancer. A multidisciplinary approach and a clinical infrastructure to manage these findings is needed to ensure adequate management.Whole-body MRI in individuals with heritable


We used linear mixed models to identify metabolites across both time-points associated with exposure to any overnutrition, using a false-discovery-rate correction (FDR) <0.20. These metabolites were included in a principal component analysis (PCA) to generate profiles and assess metabolite profile differences with respect to overnutrition typology . Fetal overnutrition was associated with 52 metabolites. PCA yielded four factors accounting for 17–27% of the variance, depending on age of measurement. We observed differences in three factor patterns with respect to overnutrition typology: sphingomyelin-mannose (8–13% variance), skeletal muscle metabolism (6–10% variance), and 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid . The sphingomyelin-mannose factor score was higher among offspring exposed to obesity vs. GDM. Exposure to obesity + GDM (vs. GDM or obesity only) was associated with higher skeletal muscle metabolism and CMPF scores. Fetal overnutrition is associated with metabolic changes in the offspring, but differences between typologies of overnutrition account for a small amount of variation in the metabolome, suggesting there is likely greater pathophysiological overlap than difference.Fetal overnutrition predisposes offspring to increased metabolic risk. The current study used metabolomics to assess sustained differences in serum metabolites across childhood and adolescence among youth exposed to three typologies of fetal overnutrition: maternal obesity only, gestational diabetes mellitus (GDM) only, and obesity + GDM. We included youth exposed in utero to obesity only (BMI ≥ 30;  Maternal obesity and Gestational Diabetes Mellitus (GDM) are interrelated metabolic conditions leading to fetal overnutrition, a term that refers to fetal exposure to excess maternal fuels including but not limited to glucose, amino acids, and lipids . AlthougRecent data indicate that there are overlapping as well as distinct changes in the metabolome of offspring ,8,9, depThe objective of this hypothesis-generating analysis was to extend current knowledge on the relationship between fetal overnutrition and offspring metabolic health by leveraging repeated untargeted metabolomics data in offspring across 6 years of follow-up. We aimed to: (1) identify a set of metabolites associated with any type of fetal overnutrition; (2) identify differences in offspring metabolite profiles with respect to maternal obesity only, GDM only, or both, thereby shedding light on distinct and shared pathways, and (3) assess the correlation of offspring metabolite profiles with conventional indicators of metabolic health to aid in interpretation of the metabolite profiles identified.The mean (±SD) age of participants at the childhood visit was 10.4 ± 1.5 years , and at the adolescent visit, it was 16.7 ± 1.2 years . Approximately half the participants were female, and 33% were exposed to any fetal overnutrition (obesity or GDM). Maternal and child characteristics by fetal overnutrition typology are presented in p-value < 0.20) (The metabolome-wide association study (MWAS) yielded 52 metabolites across childhood and adolescence that were significantly associated with exposure to any fetal overnutrition (obesity or GDM) versus no exposure to overnutrition  . These mWe implemented the PCA on the 52 metabolites identified from the MWAS and selected six factors to retain at each visit based on the break in Scree plot and shown in Following qualitative assessment of the top loading metabolites, we noted consistency in factor composition for four factors across the two visits: γ-glutamyl-peptide factor, sphingomyelin-mannose factor, skeletal muscle metabolism factor, and the 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) factor. The first factor, which accounts for the most variation in metabolites associated with fetal overnutrition , was driven by compounds in the γ-glutamyl-peptide amino acid subclass  was well as some phospholipids and fatty acids.Although the γ-glutamyl-peptide factor score was lower among offspring of women with obesity only vs. GDM only , covariaWe found that the other three factors did differ by type of fetal overnutrition in both unadjusted  and adjup-values for interaction terms >0.1)After excluding women with type-one diabetes, the estimates with GDM only as the reference were attenuated, but with no impact on statistical significance. After adjustment for offspring BMI , the difference in the sphingomyelin-mannose factor scores between offspring of women with obesity only vs. GDM only was attenuated to null . No othePearson correlations of the four metabolite factors of interest with conventional metabolic biomarkers and body composition are shown in In this longitudinal study of 440 mother–offspring pairs, we sought to identify sustained differences in serum metabolites across childhood and adolescence among youth exposed to fetal overnutrition, and explored differences in metabolite profiles with respect to typology of fetal overnutrition . Exposure to any fetal overnutrition was associated with persistent metabolic changes in the offspring, but further differences between typologies of overnutrition account for a relatively small amount of variation in the metabolome, suggesting that there is likely a greater degree of pathophysiological overlap than distinct differences.The greatest amount of variation in metabolites associated with developmental overnutrition was accounted for by the γ-glutamyl-peptide factor, although this factor did not differ by overnutrition typology after adjustment for confounders. Key metabolites in this factor were derivates of glutamate , lysine , and glycine (gluconeogenic amino acid). GDM has been associated with a higher glutamine/glutamate ratio in cord blood  and glutMethionine sulfoxide was also a top loading metabolite in the γ-glutamyl-peptide factor. This amino acid metabolite is a resulting compound of oxidation by reactive oxygen species that influences redox homeostasis and regulates many metabolic pathways including protein synthesis . In miceInterestingly, we found that the correlation of the γ-glutamyl-peptide factor with indicators of metabolic health in offspring was in a direction indicative of a more favorable metabolic profile—lower adiposity, increased insulin sensitivity, and greater physical activity. In children, plasma levels of glutamine, a precursor to glutamate, were higher following a reduction in BMI . Indeed,Although there was no unifying theme among the metabolites in this factor, sub-classes of metabolites in this pattern have been linked to obesity and metabolic risk ,21,22. SInterpretation of our findings with this factor in the context of previous studies is somewhat difficult given that prior studies have used different indicators of fetal overnutrition , and metabolite profile composition and choice of targeted assays has been study-specific. In a pre-birth cohort of 330 mother–offspring pairs with metabolomics measured in childhood (5–7 years of age), a metabolite profile characterized by phosphatidylcholines, plasmalogens, sphingomyelins and some ceramides was positively correlated with maternal pregnancy weight gain, but negatively correlated with fasting glucose . These fKey compounds in this factor included α-hydroxyisocaproate and 2-hydroxy-3-methylvalerate, ketoacid metabolites of the branched-chain amino acids (BCAA) leucine and isoleucine; malate and citrate, TCA cycle intermediaries ; and uraCMPF and its hydroxylated metabolite hydroxy-CMPF are metabolites of furan and long-chain omega-3 fatty acids ,32. AlthOur study has several strengths. First, the repeated metabolomics data are a unique asset of the EPOCH cohort that allowed us to examine the association of fetal overnutrition across two sensitive life stages for development of metabolic disease risk: childhood and adolescence . Assessmn = 440), especially in comparison to other metabolomics analyses of intrauterine exposures and outcomes in youth (n for most <350)  = 2). In this analysis, we retained 637 metabolites at both the childhood and adolescent visit that were annotated. All metabolite processing was performed using R .Details on the untargeted metabolomic profiling in the EPOCH cohort have been published ,48. BrieFasting triglycerides (TGs), total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), and glucose were measured using enzymatic kits. Insulin was measured using a radioimmune assay, and leptin and adiponectin were measured using a Multiplex assay kit, all by Millipore Corporation . We calculated the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). At both research visits, research assistants measured offspring’ blood pressure twice in the sitting position using an oscillometric monitor (Dinamap ProCare V100).2, age- and sex-specific BMI z-scores were derived using the World Health Organization (WHO) growth reference for children aged 5–19 years [At each research visit, offspring waist circumference, triceps and subscapular skinfold thickness, height, and weight were measured. BMI was calculated as kg/m19 years , and theAt each research visit, total energy intake  was assessed using the Block Kid’s Food Questionnaire . AverageMaternal level of education, total household income, and smoking at any time during pregnancy were self-reported during the childhood visit. Offspring race and ethnicity, sex, and date of birth were self-reported at the first research visit.We assessed bivariate associations of maternal and offspring characteristics with typology of fetal overnutrition and tested for statistical differences using an ANOVA for continuous variables and Pearson chi-squared tests for categorical variables. This step, in conjunction with prior knowledge of determinants of metabolic health in youth, informed covariate selection for multivariable analysis. We then conducted the analysis in three sequential steps outlined below.1 = intercept; x2 = fetal overnutrition category ; X3 = a vector of time-invariant factors ; x4 = offspring age for individual i at time j; ε = error term i at time j.We implemented a metabolome-wide association study (MWAS) using linear mixed models to identify offspring metabolites across the childhood and adolescent visits that were persistently associated with exposure to any type of fetal overnutrition.For these models, the outcome (Y) is repeated assessments of the metabolites for individual i at time j (childhood and adolescent visits), βs are the main independent variable/exposure of interest , and covariates . We included offspring ID as a repeated subject statement to account for correlation between metabolites from the same individual with an unstructured correlation matrix. Considering the number of tests and high degree of correlation among metabolites, we employed a FDR described by Benjamini and Hochberg (1995) at level α = 0.20 .First, we consolidated the metabolites from Step 1 into distinct metabolite profiles using principal component analysis (PCA), an unsupervised dimension-reduction approach that creates latent variables  based on their intercorrelations. This procedure, completed separately for metabolite data at the childhood and adolescent visit, reduced the number of subsequent comparisons and enhanced interpretability given that metabolites on related metabolic pathways are correlated with one another. Upon creation of the PCA factors, we determined the number of factors to retain at each visit based on visual inspection of the Scree plots. To interpret the factors, we assessed the composition of each factor and focused on metabolites with positive factor loadings ≥0.40.When assessing the composition of each factor at the childhood and adolescent visits, we found that despite slight differences in the factor order—an indicator of the amount of variation explained by each factor—there were factors with the same combination of high-loading metabolites at both visits. For instance, the high-loading metabolites of the first factor at the childhood and adolescent visit were the same, as was the case for high-loading metabolites of the third factor at the childhood visit and the second factor of the adolescent visit, despite slight variation in the exact degree of loading. These factors, for which metabolite composition was similar across the two visits, were of interest as they represent persistent metabolic differences in the offspring’s metabolite profile that spanned both time points.We then used linear mixed models to examine associations of fetal overnutrition typology with the metabolite factors across the childhood and adolescent visits. In the models, fetal overnutrition was categorized as a four-level parameter: obesity and GDM, obesity only, GDM only, and neither obesity nor GDM (reference) and repeated metabolite factors were the outcome. We used contrast statements to compare the difference in our response variable (metabolite factors) between different levels of the fetal overnutrition parameter. This allows us to compare the difference in metabolite factor between all three different combinations of our fetal overnutrition parameter. The contrasts used the following specification: (1) obesity and GDM vs. GDM only, (2) obesity and GDM vs. obesity only, and (3) obesity only vs. GDM. If a factor was associated with a specific fetal overnutrition typology, we then further explored differences in individual metabolites from that factor. We assessed unadjusted associations followed by adjusted associations after accounting for maternal prenatal smoking, and offspring sex, age, and ethnicity and race.The following sensitivity analyses were conducted. Seven women in the GDM-only typology had type-one diabetes; we excluded them in multivariable models to assess if potential etiological differences of hyperglycemia in pregnancy impacted the conclusions. Second, although birthweight, BMI, kilocalories, and physical activity are potential mediators to the relationship between fetal overnutrition and metabolite profiles during childhood and adolescence, we assessed the impact of including birthweight-for-gestational-age z-score, and repeated measures of BMI, kilocalories, and physical activity in sperate multivariable models. Third, in multivariable models we additionally adjusted for Tanner stage at the childhood and adolescent visits. Fourth, we assessed the impact of adjustment for GDM treatment in the multivariable models. Lastly, we tested for an interaction with offspring sex in unadjusted models to assess for evidence that the association between fetal overnutrition and metabolite profiles/factors scores was different in males versus females.We used Pearson correlation coefficients to inform interpretation of offspring metabolite profiles captured by the factors and their correlation to concurrently measured conventional indicators of metabolic health . Statistical analyses were performed with SAS version 9.4 .


Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic sequencing data often achieve varying results: high accuracy on some antibiotics but relatively low accuracy on others. Traditional machine learning (ML) approaches have been applied to classify drug resistance for MTB and have shown more stable performance. However, there is no study that uses deep learning architecture like Convolutional Neural Network (CNN) on a large and diverse cohort of MTB samples for AMR prediction. We developed 24 binary classifiers of MTB drug resistance status across eight anti-MTB drugs and three different ML algorithms: logistic regression, random forest and 1D CNN using a training dataset of 10,575 MTB isolates collected from 16 countries across six continents, where an extended pan-genome reference was used for detecting genetic features. Our 1D CNN architecture was designed to integrate both sequential and non-sequential features. In terms of F1-scores, 1D CNN models are our best classifiers that are also more accurate and stable than the state-of-the-art rule-based tool Mykrobe predictor . We applied filter-based feature selection to find AMR relevant features. All selected variant features are AMR-related ones in CARD database. 78.8% of them are also in the catalogue of MTB mutations that were recently identified as drug resistance-associated ones by WHO. To facilitate ML model development for AMR prediction, we packaged every step into an automated pipeline and shared the source code at https://github.com/KuangXY3/MTB-AMR-classification-CNN.Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based  Previous work estimated that the deaths attributable to antimicrobial resistance might rise from the current estimate of 700,000 lives per year to ten million annually by 20502. The prevalence of bacterial strains’ resistance to antibiotics has reduced the efficacy of antibiotics treatment dramatically3, which leads to the urgent need for antimicrobial susceptibility testing to guide the treatment of antibiotics for serious bacterial infections. The conventional culture-based methods have limitations including extended turnaround time for slow-growing bacteria such as Mycobacterium tuberculosis (MTB) and bias due to potential contamination. MTB remains the world’s most deadly infectious disease, with an estimated 1.5 million deaths in 20194. The currently recommended treatment for drug-susceptible TB disease is a 6-month course of four first-line drugs: isoniazid (INH), rifampicin (RIF), ethambutol (EMB) and pyrazinamide (PZA)5. As resistance to first-line drugs has become more prevalent, second-line drugs were developed to treat first-line drug-resistant TB disease, which requires a course of second-line drugs for at least nine months and up to 20 months4. The emergence of drug-resistant TB continues to threaten global TB control efforts. The World Health Organization reported that nearly half a million people developed rifampicin-resistant TB (RR-TB), of which 78% had multidrug-resistant TB (MDR-TB) around the world in 20194. There is an urgent need to rapidly identify drug sensitivity profiles of TB, given the fact that culture-based diagnostic tests are usually time-consuming.Antimicrobial resistance (AMR) is recognized as one of the greatest concerns for public health globally6. These methods are based on the identification of variants associated with AMR from whole genome sequencing (WGS) data. The WGS data from clinical strains has been curated in dedicated databases including the Comprehensive Antibiotic Resistance Database (CARD)7 and the Pathosystems Resource Integration Center (PATRIC) 8.To overcome these restrictions and identify antibiotic resistance more efficiently, researchers use conventional association rule methods to predict antimicrobial resistance10. Yang et al. developed and compared different traditional ML methods using a cohort of 1839 UK MTB isolates for the prediction of resistance on eight anti-TB drugs. Kouchaki et al. trained their models by using a dataset of over 13,402 isolates for more stable prediction on seen and unseen samples10. Three basic ML classifiers based on the feature space after dimension reduction and three ensemble learning methods were considered on this dataset. Another study conducted by Zhang et al. investigated deep learning strategy by using 2D Convolutional Neural Network (CNN) on whole-genome sequencing data of 149 MTB isolates for resistance classification on a less studied drug PZA11. Variants were called by aligning reads on a single reference genome H37Rv. Although ML, including deep learning, has been applied to the prediction of AMR, most studies used a limited number of isolates collected from a specific area, and all of them used single strain reference when detecting variants instead of using pan-genome reference13, which could result in poor mapping and variant calling quality in new strains. The use of a pan-genome reference can decrease errors in the mapping and variant detection process, especially for more diverged strains.Traditional machine learning (ML) algorithms, e.g., support vector machine (SVM), logistic regression (LR) and random forests (RF), have been compared with variant-based association rules for AMR prediction using WGS data of pathogen isolates in recent years14. Mykrobe predictor uses a De Bruijn graph representation of bacterial diversity to identify species and resistance profiles of clinical isolates for Staphylococcus aureus and Mycobacterium tuberculosis. We used a dataset of 10,575 MTB isolates15, which is imbalanced with more susceptible isolates than resistant ones for all four first-line drugs mentioned above and four second-line drugs: amikacin (AMK), capreomycin (CM), kanamycin (KM) and ofloxacin (OFX). To reduce computation, we performed feature selection first to reduce the dimensions of input data and applied multi-input 1D CNN. Instead of using a single strain reference, we used all references from CARD database16, even including references of other bacteria to build reference clusters as a pan-genome reference. Sequencing reads were then aligned to these reference clusters for variant detection. The results showed that our best ML classifiers outperformed the state-of-the-art rule-based method Mykrobe predictor, especially for EMB resistance, and showed more stable accuracy to all the four first-line drugs. Although our basic 1D CNN architecture didn’t significantly outperform our traditional ML methods LR and RF, there are potential ways to optimize it in the future, e.g., hyperparameter tuning.Here, we present our study of MTB drug resistance classification using traditional ML methods (LR and RF) and a deep neural network architecture of 1D CNN on a large and diverse dataset of MTB isolates. To compare the performance of our ML classifiers with a state-of-the-art statistical modeling method Mykrobe predictor, we evaluated the accuracy of Mykrobe predictor on the same dataset17 and obtained corresponding lineage and phenotypic drug susceptibility test (DST) data from CRyPTIC Consortium and the 100,000 Genomes project in an excel file, which is also available in the supplementary of their publication15. The phenotypic DST results for the drugs were used as labels when training and evaluating our ML models. All the data were collected and shared by the CRyPTIC Consortium and the 100,000 Genomes Project15. Like the datasets used by previous studies, this dataset is imbalanced in that most isolates are susceptible, and the minority of them are resistant for all the four first-line drugs  data for 10,575 MTB isolates from the sequence read archive (SRA) database18. ARIBA is a very rapid, flexible and accurate AMR genotyping tool that generates detailed and customizable outputs from which we extracted genetic features. First, we downloaded all reference data from CARD, which included not only references from different MTB strains but also from other bacteria . Secondly, we clustered reference sequences based on their similarity. Then we used this collection of reference clusters as our pan-genome reference and aligned read pairs of an isolate to them. For each cluster that had reads mapped, we ran local assemblies, found the closest reference, and identified variants. After running these steps, ARIBA generated files including a summary file for alignment quality, a report file containing information of detected variants and AMR-associated genes, and a read depth file. For each cluster, the read depth file provides counts of the four DNA bases on each locus of the closest reference where reads were mapped.To detect the potential genetic features that could contribute to MTB drug resistance classification, we used a command-line tool called ARIBA18. From these high-quality mappings, we collected novel variants in coding regions, well-studied resistance-causing variants and AMR-associated gene presences that were detected from at least one out of the 10,575 isolates as 263 genetic features. In addition, we included indicator variables for each of the 19 lineages into our feature vector resulting in a total of 282 features.Next, we filtered out low-quality mappings that did not pass the ‘match’ criteria defined in ARIBA’s GitHub wikiWe applied two traditional ML algorithms, RF and LR, on the sample sets labeled with phenotypic DST results see “” section20. A final prediction is an average or majority vote of the predictions of all trees. It is often used when there are large training datasets and a large number of input features. Moreover, RF is good at dealing with imbalanced data by using class weighting. Here we trained each RF classifier with 1000 estimators.RF is an ensemble method and made up of tens or hundreds of estimators (decision trees) to compress overfitting21. By using a sigmoid function (logit), linear regression is transformed into logistic regression so that the prediction range is  for outputting probabilities. Then, LR model is fitted using maximum likelihood estimation. During the training process, we applied L1 regularization on LR models for feature selection and to prevent overfitting22.LR is a popular regression technique for modeling binary dependent variable23. When we say CNN, generally, we refer to a 2-dimensional CNN, which is often used for image classification. However, there are two other types of CNN used in practice: 1-dimensional and 3-dimensional CNNs. Conv1D is generally used for time-series data where the kernel moves on one dimension and the input and output data are 2-dimensional. Conv2d and 3D kernels move on two dimensions and three dimensions, respectively.CNN is a class of deep neural networks that takes multi-dimensional data as input24. Then, we tuned the feature importance cutoff to find the one that maximizes the F1-score of an RF model trained on the remaining 20 percent of samples. For each of the eight drugs, features were selected when their feature importance scores were bigger than the optimal cutoff. The tuning processes for first-line drugs are visualized in Fig. Because deep learning algorithms require substantial computational power, we performed feature selection to only keep relevant features as input for deep learning algorithms. First, we randomly selected 80 percent of samples to calculate the importance of each feature by using the scikit-learn RF feature importance function that averages the impurity decrease from each feature across the trees to determine the final importance of each variable25 that took N inputs of 4 × 21 matrices representing N selected SNP features into the first layer. Each 4 × 21 matrix consists of normalized DNA base counts for each locus within a 21-base reference sequence window centered on the focal SNP . By using a pan-genome reference, we can get more reliable alignments to detect variants more accurately27. The 6291 isolates with phenotypic DST results available for the four first-line drugs were clustered into a phylogenetic tree , 68 (INH), 113 (PZA) and 125 (EMB) drug-specific features were collected. Across these four sets, there were 42 shared features, indicating that the 42 features selected for RIF resistance prediction are also relevant to AMR classification of the other three drugs  drugs and three different ML algorithms: LR, RF and customized 1D CNN. The four datasets described in “We calculated different metrics to measure the performances of the four approaches Table . The F1-In their manuscript, the Mykrobe predictor authors stated that the sensitivity of their MTB drug resistance prediction was low, potentially because their graph-based association rule had limited understanding of the underlying genetic mechanisms. We confirmed their observation when testing Mykrobe predictor on our datasets. As shown in Table 29) to compare with the filter-based one used in this study30. Because ARIBA was not focusing on detection of low-frequency variants in NGS data and low-frequency variants are also associated to AMR classification, we could add low-frequency variants as additional features for training our ML model by using specific SNP detection tool like binoSNP31 in our future work.According to our tenfold cross-validation, our best ML classifiers showed a substantial increase in the F1-score for all the four first-line drugs and one second-line drug when compared to the prediction from the state-of-the-art rule-based method Mykrobe predictor. Our 1D CNN architecture only slightly outperformed the traditional ML methods LR and RF, although it requires more intensive computing resources during the training process. To reduce the computing resource requirements, we performed feature selection to remove irrelevant features before training 1D CNN models. For each drug, all selected variant features are known variants based on the current version of CARD. In this study, a special 1D CNN architecture was built to fit our data structure of mixed-type of data . As our first-stage study for MTB AMR classification, we didn’t perform hyperparameter optimization, but it is a potential way to improve our models in the future. In addition, we can include novel variants on non-coding regions and larger variants (e.g. indel) as additional features and try the computationally expensive wrapper-type feature selection algorithms . In many clinical settings both specificity and sensitivity have critical impacts on patients and the care they receive. We also presented the G-score which is simply the geometric mean of sensitivity and specificity; however, interpretation may be biased in cases where there is an imbalance of classes . When focusing on this metric, there is more variability in performance outcomes between the rule-based and the ML methods presented here. Regardless, across all these methods substantial gains in specificity are possible and should be a focus of future work in this area.Finally, we automated the whole process, from data collection to model training and evaluation, into a flexible pipeline that can be easily updated with new strains or train AMR prediction models of different antibiotics for other bacteria , our CNN architecture slightly outperformed the other two traditional ML algorithms. As a result of variant analysis, 78.8% of variant features selected for our CNN model training are also identified as TB drug resistance-associated ones by WHO.Supplementary Information.


Since the licensure of the varicella vaccine in the United States in 1995 and the implementation of the universal varicella vaccination program, varicella infection rates, and associated morbidity and mortality rates have decreased. However, controversy exists over whether universal vaccination has resulted in an increased incidence of herpes zoster (HZ). In 1965, Dr. Hope-Simpson hypothesized that exogenous exposures to the wild-type varicella-zoster virus (wt-VZV) provide immune boosts that inhibit HZ; therefore, reducing the amount of circulating wt-VZV may have the negative effect of increasing the incidence of HZ. A historical review of data from the Centers for Disease Control and Prevention-sponsored Antelope Valley Varicella Active Surveillance Project, along with other studies, is provided to investigate the exogenous boosting hypothesis in the first decade post-vaccine licensure. These data indicated that adoption of universal varicella vaccination led to (1) significant HZ incidence rate increases among children, adolescents, and adults with a history of wild-type varicella and (2) decline in varicella vaccine efficacy after the initial post-licensure period. These effects were likely due to reduced exogenous exposures from children shedding wt-VZV. Appropriate methodologies for ongoing research are also discussed, both in studies during the first decade post-licensure and more recent work. Varicella-zoster virus and initiation of the universal vaccination programThe varicella-zoster virus (VZV) is responsible for both varicella (chickenpox) and herpes zoster (HZ) (shingles). Primary infection with VZV as varicella usually occurs during childhood and generally causes mild disease. Serious complications are more common when primary infection occurs in infancy (≤18 months) or adulthood. After symptoms resolve, VZV remains in latent form in sensory ganglia and may reactivate to cause HZ, especially as immunity decreases over the individual’s lifetime or if the individual is immunocompromised.In 1965, Dr. Hope-Simpson, a British general practitioner who meticulously tracked cases of HZ occurring in his clinical practice in Cirencester for 16 years, reported age-stratified HZ incidence rates. The step-wise increases in HZ incidence rates in successively older age categories likely led to his hypothesis that exposure to circulating VZV and incidence of HZ were inversely linked. He suggested that two mechanisms--exogenous exposures  and asymptomatic endogenous reactivations --were involved in boosting immunity -8.Journal of Pediatrics also laid out the concerns about the effect of universal varicella vaccination on HZ incidence:Despite prior concerns that reducing exogenous exposures to wild-type varicella might increase the incidence of HZ, the United States licensed the varicella vaccine and recommended it for universal use in 1995. The Summary for Basis of Approval Agreement between the Food and Drug Administration (FDA) and Merck (the varicella vaccine manufacturer) acknowledged the concern "that universal vaccination might result in increased rates of herpes zoster in vaccinated and unvaccinated individuals" [The incidence of zoster in vaccinated and unvaccinated individuals might increase after universal immunization. There is evidence that reexposure to natural chickenpox boosts cellular immunity and potentially reduces an individual's likelihood of having zoster. Vaccine-induced herd immunity will reduce exposure to wild-type varicella; mathematical modeling indicates that the frequency of zoster in adults could increase .Despite concerns acknowledged by the FDA, Merck, and other health authorities regarding the effect that a loss of exogenous boosts following universal varicella vaccination might have on the incidence of HZ, universal varicella vaccination was adopted in the United States. The program was expected to be cost-effective, with cost savings from decreasing parental time lost from work to care for a child with chickenpox . The iniImpact of universal varicella vaccination in the first decade post-licensure: The Antelope Valley Varicella Active Surveillance ProjectIn late 1994, the Centers for Disease Control and Prevention (CDC) and the Los Angeles County Department of Health Services (LADHS) formed a cooperative agreement to establish the Antelope Valley (AV) Varicella Active Surveillance Project (VASP). This project’s mission was to perform epidemiological studies and monitor the effects of the universal varicella vaccination program on the 300,000 residents comprising the study population within the AV region , beginning in 1995. Dr. Goldman, Research Analyst for VASP, in response to long-time nurses reporting for the first time in 1999 that they were encountering a surprising number of HZ cases among school-aged children, proposed adding active surveillance of HZ. With CDC approval, HZ surveillance began in January 2000. Goldman resigned in October 2002 due to concerns that the study data were not being presented accurately.Baseline HZ dataIdeally, the same population-based, active surveillance data should be used to compute and compare HZ incidence rates both pre- and post-varicella vaccine licensure. Unfortunately, CDC did not initially authorize VASP to conduct active surveillance of HZ. Thus, no baseline HZ incidence data exists for the AV study region during the pre-licensure pre-1995) and early post-licensure years (1995-1999). However, there are three "surrogate" HZ incidence rates available--two from historical studies in other populations 995 and e, and oneThe three methodologically different studies, conducted in dissimilar populations, during different pre-varicella vaccine time periods, reported nearly identical HZ incidence rates among individuals aged <20 years. Despite limitations inherent to the VASP-sponsored survey--which investigated susceptibility to varicella and HZ incidence rates--respondents reflected the socio-economic and racial balance of the AV population and the collected data appear to have accurately captured the true HZ incidence rates among children during the pre-varicella vaccine licensure years and among individuals aged 10-14 years in the early post-licensure period .Increased HZ incidence rates after vaccine licensure among children, adolescents, and adults with a history of varicellaBy 2000, the varicella vaccine had been administered to approximately 50% of children aged <10 years in the Antelope Valley study population. Varicella incidence had declined by 80% and the characteristic seasonal peaks were no longer distinguishable. Thus, the VASP was positioned to acquire HZ case reports among children, adolescents, and adults who were now predominantly experiencing internal boosts (asymptomatic endogenous reactivations) in the near absence of exogenous boosts. These post-licensure conditions allowed Dr. Hope-Simpson’s 1965 hypothesis to be tested.Using Hope-Simpson as a reference, HZ incidence rates in the pre-varicella vaccine licensure era were more accurately represented by a relatively constant true HZ incidence rate (using rounded figures) of 140 cases per 100,000 p-y among individuals aged 1-19, then nearly doubling to 260 cases per 100,000 p-y among adults aged 20-49, then nearly doubling again to 500 cases per 100,000 p-y among adults aged 50-59 [From 2000 to 2001, HZ cases reported to VASP either maintained or increased in every adult 10-year age category  much higher than HZ rates in vaccinated children, and (2) greater than HZ rates during the pre-licensure era. Yet, even after the varicella vaccine program matured (from 2000-2002), the CDC advocated the calculation of a single crude HZ incidence rate among all children aged <10 years whether vaccinated or having a history of varicella . This apMethodology for assessing population HZ incidence rates: capture-recaptureThe reported incidence rates in most epidemiological studies (including active surveillance) are extremely poor, missing up to 90% of the cases, with a high degree of variation -29. The JAMA, CDC/VASP used capture-recapture analysis as evidence that reporting completeness had increased over the study period and, therefore, decreases in varicella incidence were not the result of decreases in the level of reporting [In the VASP, capture-recapture consistently demonstrated 50% reporting completeness (ranging from 43% to 62%) overall child and adolescent age categories and years (2000-2001). In a study published in eporting . Since teporting .unadjusted HZ incidence rates analyzed and reported by Goldman (2000-2003), indicating that the reported ascertainment-corrected rates were likely closer estimates of population rates [Additional longitudinal VASP data analyzed and reported by the CDC 2000-2006) corroborated the early cumulative on rates . Both thon rates -20. 00-2006 chalf the true population rates. In 2009, when the CDC first published age-specific HZ incidence rates among children and adolescents annually for 2000-2006, CDC/VASP authors compared VASP unadjusted HZ incidence rates to rates reported in other studies using methodology with more exhaustive case collection [By not adjusting for 50% under-reporting of HZ cases to VASP, the HZ incidence rates reported by CDC/VASP were llection ,20, prodllection ,23.Decreased vaccine efficacy after early "honeymoon period"Exogenous exposures to wild-type varicella appear to have not only boosted cell-mediated immunity to postpone or prevent the reactivation of VZV as HZ in individuals who had previously contracted varicella, but they also augmented efficacy of the varicella vaccine. In 2004, the CDC published a study on the contagiousness of varicella within households, and reported a mean accumulative varicella vaccine efficacy of 78.9%  during 1997-2001 . UnfortuFrom 1997 to 1999 there was a "honeymoon" effect, where vaccine efficacy increased from 87% to 96%, augmented due to vaccinees receiving exogenous exposures from children infected with wild-type varicella . However, in 2000 and beyond, as more children were vaccinated and the widespread circulation of wild-type varicella declined, exogenous exposures became rare and single-dose vaccine efficacy experienced a >10% annual drop-off in efficacy from 96% in 1999 to 74% in 2001. While not significant at the 95% confidence level (z = 1.96), as noted in the CDC publication , the decAugmentation of vaccine efficacy by exogenous boosting can only occur when varicella remained endemic, constantly circulating in the environment. Ironically, the "success" of the varicella vaccine in reducing cases of wild-type varicella contributed to the failure of the single-dose vaccine to maintain adequate efficacy to prevent varicella in vaccinated individuals. Thus, starting in 2006, a booster dose of the varicella vaccine was recommended for children aged four to six years. The declining efficacy and necessity for a booster dose dramatically changed the cost-benefit analysis of the universal vaccine program . Based oThe impacts of universal varicella vaccination in the first decade post-licensure: Other studiesStudies Reporting No Increase in HZ IncidenceTwo studies, the Massachusetts Department of Public Health (MDPH) and Group Health Cooperative (GHC) in Seattle, Washington, have been cited multiple times by CDC as having reported no increases in HZ incidence rates in any age group -37. HoweIn contrast to the small sample size and limited observation time  of the MDPH survey , AV-VASPStudies Reporting Increased HZ IncidenceIn addition to VASP, three studies among adults, two of which were conducted in different U.S. populations using different methods, found statistically significant increases in HZ incidence rates during the first decade post-licensure ,18,40,41Beyond the First Decade (2005-Present)In 2008, Reynolds et al., CDC authors, stated there was "evidence from population-based studies that rates of HZ were increasing in the US before the introduction of the varicella vaccine program"  and citeIn 2007, a study by Yawn et al., sponsored by the Mayo Clinic, based on the Rochester Epidemiologic Project (REP) dataset from Homestead County, Minnesota, reported an overall 28.1% increase in the HZ incidence rate , ranging from 320 (95% C.I. 290-350) cases/100,000 p-y in 1996/1997 to 410 (95% C.I. 380-440) cases/100,000 p-y in 2000/2001 among adults aged > 22 years , sponsorThe Kawai et al. study reported HZ incidence rates among all individuals, including varicella-vaccinated children . With eaSince different researchers drew contradictory conclusions from the analysis of the same REP database, this raises the question of publication bias that tends to the favorable outcome: demonstrating that universal varicella vaccination had no impact on the HZ epidemiology. Other CDC-sponsored retrospective studies -49 also Predicted Impacts of the Increased Acceleration of HZ IncidenceDepending on the study design and methodology, we have observed post-licensure annual HZ incidence rate increases ranging from 5.6%  to 28.5%The incidence of HZ in vaccinated adults remains unknown and will need to be studied as the first vaccinated cohort ages. Reactivation of v-VZV has been documented and can lead to HZ ,53,54. WThe universal varicella vaccination program in the AV region provided suitable conditions for the CDC-sponsored VASP to gather the first data that could lead to support or rejection of Dr. Hope-Simpson’s 1965 hypothesis that age-specific HZ incidence rates are dependent on the frequency of each cohort’s exposure to individuals shedding wt-VZV. Unfortunately, in study after study, insights on the significance of exogenous boosting were minimized, suppressed, or misrepresented to make unfavorable outcomes of the universal varicella vaccination program appear less concerning than they actually were. This fueled the exogenous boosting debate and prolonged the controversy. Moreover, it created the impression that CDC/VASP manipulated the scientific data to prevent publication of data that could adversely influence immunization rates, regardless of the potential public health consequences.There is no question that the US universal varicella vaccination program reduced cases of chickenpox. However, any attributed benefits were disproportionately offset by significant HZ incidence rate increases among adults during the first decade post-licensure. Future surveillance and methodologically robust studies will be needed to corroborate the preliminary evidence gathered during the first decade post-licensure in support of Hope-Simpson’s exogenous boosting hypothesis. If exogenous boosts from children shedding wt-VZV are confirmed to be essential to maintaining cell-mediated immunity, universal varicella vaccination may become costly as additional booster doses of the varicella vaccine and/or HZ vaccine are added to provide similar protection that was achieved naturally and at no cost by endemic exogenous exposures in the pre-licensure era.


RTS,S/AS01 (GSK) is the world’s first malaria vaccine. However, despite initial efficacy of almost 70% over the first 6 months of follow-up, efficacy waned over time. A deeper understanding of the immune features that contribute to RTS,S/AS01-mediated protection could be beneficial for further vaccine development. In two recent controlled human malaria infection (CHMI) trials of the RTS,S/AS01 vaccine in malaria-naïve adults, MAL068 and MAL071, vaccine efficacy against patent parasitemia ranged from 44% to 87% across studies and arms . In each trial, RTS,S/AS01 immunogenicity was interrogated using a broad range of immunological assays, assessing cellular and humoral immune parameters as well as gene expression. Here, we used a predictive modeling framework to identify immune biomarkers measured at day-of-challenge that could predict sterile protection against malaria infection. Using cross-validation on MAL068 data , top-performing univariate models identified variables related to Fc effector functions and titer of antibodies that bind to the central repeat region (NANP6) of CSP as the most predictive variables; all NANP6-related variables consistently associated with protection. In cross-study prediction analyses of MAL071 outcomes , top-performing univariate models again identified variables related to Fc effector functions of NANP6-targeting antibodies as highly predictive. We found little benefit–with this dataset–in terms of improved prediction accuracy in bivariate models vs. univariate models. These findings await validation in children living in malaria-endemic regions, and in vaccinees administered a fourth RTS,S/AS01 dose. Our findings support a “quality as well as quantity” hypothesis for RTS,S/AS01-elicited antibodies against NANP6, implying that malaria vaccine clinical trials should assess both titer and Fc effector functions of anti-NANP6 antibodies. Plasmodium parasites , are transmitted to humans through the bite of infected female Anopheles mosquitoes  African Region . The causquitoes . Althougsquitoes .P. falciparum by eliciting antibody and T-cell responses to circumsporozoite protein (CSP), the major surface protein of sporozoites  is the most clinically advanced vaccine to date  and targrozoites . In a phrozoites . RTS,S/Arozoites  and was rozoites .Controlled human malaria infection (CHMI) studies are a valuable tool for identifying correlates of protection, including in the context of vaccine development . MAL068 P. falciparum mosquitoes and monitored for parasitemia through Day 28 post-challenge. In the RRR arms, VE against malaria infection was 52% (95% CI 25–70%) in MAL068 and 63% (29–80%) in MAL071. In the alternative arms, VE against malaria infection was 44% (21–60%) in the ARR arm of MAL068 and 87% (67–95%) in the RRr arm of MAL071.Each trial included a standard arm with three RTS,S/AS01 doses administered at one-month intervals (RRR arm), as well as an alternative arm with a modified version of this regimen . In MAL0A large body of work , and day+ T cells, B cells, and dendritic cells to the T zone—B-cell follicle interface  cell development at early time points and is crucial for the generation of extrafollicular plasma cell responses  is crucial for development of the protective CSP-specific pTfh and memory B cell responses seen by Du et al. reported that the MX2/GPR183 expression ratio in peripheral blood mononuclear cells (PBMCs) at 1 day post-third dose discriminated protected from non-protected RTS,S/AS01 recipients in five independent CHMI trials, including MAL068 and MAL071 . Specifinterface . In CD4+esponses . Relatedesponses . These fKazmin et al. reported an inverse correlation with protection of natural killer (NK) cell-related blood transcriptional module expression in PBMCs, in both arms of MAL068 , generatvia opsonophagocytic pathways such as antibody-dependent cellular phagocytosis (ADCP) of parasites can have widely divergent outcomes  using R package lme4, pooled across arms and 3) Selection of measures with evidence of vaccine-induced response , for each participant. The following immunogenicity model was used: ∼Arm + Day + Sex + Age. P values and FDR-corrected P values were calculated with the limma package. For down-selection of the 345 BTMs, we selected BTMs with significant immunogenicity on day of challenge .To use RNA-seq data . Moreover, in MAL068, the top 20 variables all associated with protection (OR > 1), whereas in MAL071, less than half (n = 8) of the top 20 variables associated with protection and the remaining (n = 12) associated with non-protection, albeit none significantly after false discovery rate (FDR) adjustment.In MAL071 (RRR and RRr groups pooled), the majority  on the data from each study to assess whether a fully unsupervized approach would show discrimination between the protected and non-protected groups of each trial and, if so, which variables contribute to such discrimination. The top 20 variables of each PC for MAL068 (RRR and ARR arms) and MAL071 (RRR and RRr arms) are listed in The PCA of MAL068 showed no discrimination between the protected and non-protected groups on principal component (PC) one or PC2 . PC1 accLike the MAL068 results, the PCA of MAL071 did not show any discrimination between the protected and non-protected groups. PC1 accounted for 26% of the total variation; most of its top variables were CSP-specific or C-term-specific Fc array variables . PC2 accBefore examining the cross-study replicability of our findings, we considered the substantial difference between the MAL068 and MAL071 results thus far. A possible reason for this difference is that the two trials compared different regimens, the protective mechanisms of which may have differed [as suggested by the findings of ]. These For the cross-study validation, all possible univariate and bivariate models were trained on MAL068  and validated on the MAL071 RRR data set. Regardless of whether the models were trained on MAL068 RRR or on the full MAL068 dataset, the bivariate models in the training set almost always had higher CV-AUC values than the univariate models, whereas little or no improvement in validation AUCs was observed in the validation dataset for the bivariate models compared to the univariate models. For example, the top-performing (CV-AUC = 0.996) bivariate model trained on MAL068 RRR (ADCP NANP6 + BAMA IgG2 CSP Avidity Index) includes variables that were each identified in the top 10-performing univariate models . As assessed by validation AUC, the ADCP NANP6 + BAMA IgG2 CSP Avidity Index model (0.822) showed no appreciable improvement over the univariate ADCP NANP6 model (0.817) . This trLooking across the variables that tended to be identified in top-performing models, we find that many variables were related to NANP6 and were associated with protection . For modPerforming the same analysis but predicting MAL071 outcomes in the RRr arm showed poorer predictive performance  of the top-performing models . The redWhen we trained models on MAL071 (RRR or both arms combined) and validated on MAL068 (RRR or both arms combined), the results were essentially the same, except that MAL071 showed a large number of RNAseq variables that ranked highly in terms of CV-AUC on MAL071, and the RNAseq variables were not consistent when applied to MAL068 (data not shown).We next explored some complementary approaches to see whether they would yield consistent results to those obtained by the workflow outlined in To further explore whether including additional immune response variables could improve cross-study model prediction, we plotted CV-AUC for the top 200 univariate, bivariate, and trivariate models trained on MAL068  vs. the validation AUC on MAL071 data  . While tBuilding upon the wealth of previous RTS,S/AS01 immune correlates analyses, here we took a predictive modeling approach that included immune response data across many different assays and assessed the contribution of each immune biomarker to prediction of individual-level outcomes of RTS,S/AS01 vaccinees in two different controlled human malaria infection studies. We identified univariate and bivariate models with good predictive accuracy across studies [validation area under the receiver operating characteristic curve (AUC) > 0.8], and our first major conclusion is that there was little to no improvement in the bivariate models compared to the univariate models, when combining all the day-of-challenge biomarker data into a comprehensive dataset. We hypothesize that the relatively small sample sizes, which in turn limited the size of the training/testing/validation sets, hindered the identification of well-performing complex models. Applying the same approach to future studies with larger sample size may enable such models to be identified. Another potential explanation for the lack of significant improvement in bivariate vs univariate models  is the high correlation between variables, as highly correlated variables will generally not improve in logistic regression over single variable models.We identified central NANP repeat region (NANP6)-targeted antibody dependent cellular phagocytosis (ADCP), binding of NANP6-targeting antibodies to the FcGRIIIa receptor, and anti-NANP6 IgG1 antibody titer as consistently predictive immune response measurements, with higher levels of each biomarker on day of challenge associated with individual-level protection after controlled malaria challenge. Our second major conclusion is thus that our data support a “quality as well as quantity” hypothesis for how RTS,S/AS01-induced antibodies may protect, in line with the previous findings of We next relate our results to the published correlates analyses of MAL068 and MAL071 that were introduced earlier. In the systems serology analysis by Moreover, consistent with the findings of Seaton et al. (under revision)Dennison et al. recently analyzed antibody avidity data from MAL071 and reported that, while no association with protection was seen with the avidity of antibody responses to CSP, NANP6, C-term, or NPNA3 in the RRR arm, higher-avidity antibodies to CSP and C-term tended to associate with protection in the RRr arm, suggesting a potential difference in the mechanisms of protection in the two arms. Our findings are consistent with those of Dennison et al. in that BLI variables related to avidity of anti-CSP or anti-C-term antibodies did not appear in top-performing  models trained on MAL068 RRR.We next discuss our findings in the context of the previous transcriptional correlates studies . A major2 fold-change (vs baseline) MX2/GPR183 ratio in protected vs. nonprotected vaccinees on day of challenge. It is thus not surprising that we did not identify any BTMs related to MX2  or GPR183 (an oxysterol receptor) among our top-predicting variables in our day-of-challenge analyses. However, similar to Du et al., we did find that our top-predicting variables were not correlated with the known correlate anti-CSP titer  cell responses, that were predictive of individual-level protection in the MAL071 trial . While wOur focus on day-of-challenge immune response measurements meant that our models did not incorporate all previously identified correlates of protection, e.g., the many transcriptional modules whose early post-vaccination  expression was found to associate with protection (or non-protection) reported by Additional limitations of our work include that our study is in the context of a highly controlled challenge environment, and there are important differences between the CHMI studies  and the current target population for which RTS,S/AS01 is licensed . Thus, it remains to be seen whether the biomarkers highlighted by our study are similarly relevant biomarkers of protection in malaria-endemic field settings. However, in the light of our finding that anti-NANP6 IgG1 titer was consistently associated with protection, Second, the high efficacy of the RRr regimen in MAL071 hindered the development of models that could discriminate protected from non-protected individuals; thus, we had to restrict our cross-study analysis to the RRR arm of MAL071. Thirdly, by focusing on day of challenge, we likely missed the opportunity to pick up earlier immune response biomarkers–especially transcriptional modules–that may have waned by day of challenge. Additional limitations include the necessity of restricting our analysis to variables that were common across the two trials, the fact that we did not assess predictive ability of individual genes in addition to the BTM analysis, and the lack of inclusion of alternative gene sets [such as Gene Ontology (GO) gene sets related to e.g., immunological signatures  or to ca