Background Salmonella enterica serotype Typhi (S Typhi) is a major public health problem in low-income and middleincome countries. We aimed to investigate the effectiveness and impact of the typhoid conjugate vaccine Typbar-TCV against S Typhi among children in an outbreak setting of extensively drug-resistant (XDR) S Typhi in Pakistan. MethodsThis cohort study was done from Feb 21, 2018, to Dec 31, 2019. A census survey of all households located in the Qasimabad and Latifabad subdistricts of Hyderabad, Pakistan, was done at baseline, and 174 005 households were registered in the census. The Typbar-TCV immunisation campaign was initiated at temporary vaccination centres and 207 000 children aged 6 months to 10 years were vaccinated from Feb 21, 2018, to Dec 31, 2018. Social mobilisers informed parents about the vaccination process. Vaccination records were maintained electronically and linked with the household census surveys. Active surveillance for suspected and blood-culture-confirmed S Typhi was established in hospitals, clinics, and laboratories to assess the following outcomes: cases of suspected typhoid fever, cultureconfirmed S Typhi, and antimicrobial resistance. An age-stratified cohort of 1100 vaccinated children was randomly selected from the vaccination registry, tested for Vi-IgG antibodies (data not reported), and followed up fortnightly (via telephone calls or household visits) until Dec 31, 2019, for ascertainment of outcomes during the study period. 20 847 vaccinated and unvaccinated children were randomly selected from the census registry as a quality control cohort and followed up from Oct 1 to Dec 31, 2019, for ascertainment of outcomes. Vaccine effectiveness against suspected, culture-confirmed, and XDR S Typhi was calculated. Findings 23 407 children from the census registry and surveillance system were included in the vaccine effectiveness analysis. 13 436 (57•4%) children were vaccinated, 12 214 (52•2%) were male, and 10 168 (43•4%) were aged 6-59 months. 5378 (23•0%) of 23 407 children had suspected S Typhi, among whom 775 (14•4%) had cultureconfirmed S Typhi and 361 (68•6%) of 526 had XDR S Typhi. Vaccine effectiveness was 55% (95% CI 52-57) against suspected S Typhi (regardless of culture confirmation), 95% (93-96) against culture-confirmed S Typhi, and 97% (95-98) against XDR S Typhi. Interpretation Typbar-TCV is effective in protecting children against S Typhi infection in an outbreak setting, and was able, with moderate deployment, to curtail a major XDR S Typhi outbreak in a densely populated setting. The vaccine shows efficacy against S Typhi irrespective of antimicrobial resistance. Funding Bill & Melinda Gates Foundation.

S evere fever with thrombocytopenia syndrome (SFTS) is an emerging tickborne disease caused by the SFTS virus (SFTSV; genus Banyangvirus, family Phenuiviridae, order Bunyavirales). The disease is prevalent in East Asia countries. It was first detected in China in 2009 and later in Japan and South Korea (1) and is suspected to be widely spread across other parts of the world (2). The recent identification of SFTSV in Xinjiang, China (3), expanded our awareness of epidemic areas of SFTS and suggested the possibility of SFTSV spreading to bordering countries like Pakistan. However, the presence of SFTSV in Pakistan has been unclear. We investigated the seroprevalence of SFTSV in humans in Pakistan. The Study For this study, we randomly collected human serum samples (n = 1,657) from 4 provinces in Pakistan during 2016-2017 (Figure). All participants were farmers of livestock (sheep, goats, cattle, buffaloes, and camels). We recorded and summarized testing results by sex, age, and geographic location (Table). The collection of human serum samples and subsequent tests were reviewed and approved by the Ethics Committees of

A targeted and timely offered treatment can be a benefitting tool for patients with acute promyelocytic leukemia (APML). Current round of study made use of potential morphological and immature fraction–related parameters (cell population data) generated during complete blood cell count (CBC), through artificial neural network (ANN) predictive modeling for early flagging of APML cases. We collected classical CBC items along with cell population data (CPD) from hematology analyzer at diagnosis of 1067 study subjects with hematological neoplasms. For morphological assessment, peripheral blood films were examined. Statistical and machine learning tools including principal component analysis (PCA) helped in the evaluation of predictive capacity of routine and CPD items. Then selected CBC item–driven ANN predictive modeling was developed to smartly use the hidden trend by increasing the auguring accuracy of these parameters in differentiation of APML cases. We found a characteristic triad based on lower (53.73) platelet count (PLT) with decreased/normal (4.72) immature fraction of platelet (IPF) with addition of significantly higher value (65.5) of DNA/RNA content–related neutrophil (NE-SFL) parameter in patients with APML against other hematological neoplasm's groups. On PCA, APML showed exceptionally significant variance for PLT, IPF, and NE-SFL. Through training of ANN predictive modeling, our selected CBC items successfully classify the APML group from non-APML groups at highly significant (0.894) AUC value with lower (2.3 percent) false prediction rate. Practical results of using our ANN model were found acceptable with value of 95.7% and 97.7% for training and testing data sets, respectively. We proposed that PLT, IPF, and NE-SFL could potentially be used for early flagging of APML cases in the hematology-oncology unit. CBC item–driven ANN modeling is a novel approach that substantially strengthen the predictive potential of CBC items, allowing the clinicians to be confident by the typical trend raised by these studied parameters.

A targeted and timely treatment can be a beneficial tool for patients with hematological emergencies (particularly acute leukemias). The key challenges in the early diagnosis of leukemias and related hematological disorders are their symptom-sharing nature and prolonged turnaround time as well as the expertise needed in reporting confirmatory tests. The present study made use of the potential morphological and immature fraction-related parameters (research items or cell population data) generated during complete blood cell count (CBC), through artificial intelligence (AI)/machine learning (ML) predictive modeling for early (at the pre-microscopic level) differentiation of various types of leukemias: acute from chronic as well as myeloid from lymphoid. The routine CBC parameters along with research CBC items from a hematology analyzer in the diagnosis of 1577 study subjects with hematological neoplasms were collected. The statistical and data visualization tools, including heat-map and principal component analysis (PCA,) helped in the evaluation of the predictive capacity of research CBC items. Next, research CBC parameter-driven artificial neural network (ANN) predictive modeling was developed to use the hidden trend (disease’s signature) by increasing the auguring accuracy of these potential morphometric parameters in differentiation of leukemias. The classical statistics for routine and research CBC parameters showed that as a whole, all study items are significantly deviated among various types of leukemias (study groups). The CPD parameter-driven heat-map gave clustering (separation) of myeloid from lymphoid leukemias, followed by the segregation (nodding) of the acute from the chronic class of that particular lineage. Furthermore, acute promyelocytic leukemia (APML) was also well individuated from other types of acute myeloid leukemia (AML). The PCA plot guided by research CBC items at notable variance vindicated the aforementioned findings of the CPD-driven heat-map. Through training of ANN predictive modeling, the CPD parameters successfully differentiate the chronic myeloid leukemia (CML), AML, APML, acute lymphoid leukemia (ALL), chronic lymphoid leukemia (CLL), and other related hematological neoplasms with AUC values of 0.937, 0.905, 0.805, 0.829, 0.870, and 0.789, respectively, at an agreeably significant (10.6%) false prediction rate. Overall practical results of using our ANN model were found quite satisfactory with values of 83.1% and 89.4.7% for training and testing datasets, respectively. We proposed that research CBC parameters could potentially be used for early differentiation of leukemias in the hematology–oncology unit. The CPD-driven ANN modeling is a novel practice that substantially strengthens the predictive potential of CPD items, allowing the clinicians to be confident about the typical trend of the “disease fingerprint” shown by these automated potential morphometric items.

Background The tet oncogene family member 2 (TET2) gene has been reported to be involved in DNA methylation and epigenetic regulation in acute myeloid leukemia (AML). Various studies have proven functional role of TET2 mutations in AML. We herein studied the frequency and genotype-phenotype correlation of TET2 gene in AML patients in Sindh, Pakistan. Patients and methods The current study was carried out at Liaquat University of Medical & Health Sciences, Jamshoro, Pakistan, in collaboration with National Institute of Blood Disease & Bone Marrow Transplant, Karachi, Pakistan, during the period from June 2019 to June 2020. A total of 130 patients diagnosed with AML were screened for TET2 mutations. Whole exome sequencing of 14 individuals was carried out to find the genetic variants in TET2 gene. The pathogenicity of the variants was predicted by SIFT, PolyPhen2, Mutation Taster and CADD Phred scores. The allele frequency of the variants was compared with global population using 1000 genomes project and Exome Aggregation Consortium (ExAC). Furthermore, exon 3 and exon 5 of the TET2 gene were sequenced by using Sanger sequencing. The findings were correlated with subtypes of AML and corresponding karyotypes. Results Through the exome sequencing, 17 genetic variants (13 SNPs and four indels) were identified in 14 individuals. Of these, four variants that is, one frameshift deletion, one frameshift insertion and two nonsense variants were novel and not present in dbSNP151 database. Three novel variants were found in exon 3 including two frameshift variants that is, p.T395fs and G494fs, predicted as deleterious by CADD Phred scores, and one stop-gain variant (p.G898X) predicted as deleterious by Mutation Taster and CADD Phred scores. One novel non sense variant (p.Q1191X) was found in the exon 5 predicted as deleterious by SIFT, Mutation Taster and CADD Phred scores. Sanger sequencing analysis revealed one novel deletion at g105233851: del.TAGATAGA, and one novel SNP g;105233861 T>G identified in the TET2 gene. Majority of the exon 3 mutations were seen in the patients diagnosed with AML with maturation, and had a normal karyotype. Conclusion TET2 mutations were identified in around 16% of the total patients of our study indicating other mechanisms being involved in pathophysiology of AML in this cohort. The TET2 mutations provide a prognostic value in determining AML classification.