This study describes a low‐cost and time‐efficient clinical sensory test (CST) battery and evaluates its concurrent validity as a screening tool to detect somatosensory dysfunction as determined using quantitative sensory testing (QST).n = 76), non‐specific neck and arm pain  and lumbar radicular pain/radiculopathy  were included. The CST consisted of 13 tests, each corresponding to a QST parameter and evaluating a broad spectrum of sensory functions using thermal  and mechanical  detection and pain thresholds testing both loss and gain of function. Agreement rate, statistical significance and strength of correlation (phi coefficient) between CST and QST parameters were calculated.Three patient cohorts with carpal tunnel syndrome  were significantly correlated with QST, with a majority demonstrating >60% agreement rates and moderate to relatively strong correlations. However, agreement varied among cohorts. Gain of function parameters showed stronger agreement in the CTS and LR cohorts, whereas loss of function parameters had better agreement in the NSNAP cohort. Other CST parameters  did not significantly correlate with QST.Some of the tests in the CST could help detect somatosensory dysfunction as determined with QST. Parts of the CST could therefore be used as a low‐cost screening tool in a clinical setting.Quantitative sensory testing, albeit considered the gold standard to evaluate somatosensory dysfunction, requires expensive equipment, specialized examiner training and substantial time commitment which challenges its use in a clinical setting. Our study describes a CST as a low‐cost and time‐efficient alternative. Some of the CST tools  significantly correlated with the respective QST parameters, suggesting that they may be useful in a clinical setting to detect sensory dysfunction. This method evaluates both loss and gain of function and comprehensively covers somatosensory sub‐modalities mediated by different primary afferents  battery compared to the QST protocol for use as a screening tool to detect somatosensory dysfunction. We achieved this in a large population of patients with pain related to different clinical pain aetiologies and related mechanisms.22.1z‐scores for QST data in the CTS cohort. Part of the data from this cohort has previously been published , non‐specific neck and arm pain (NSNAP) and lumbar radicular pain with or without radiculopathy (LR). These cohorts were chosen to reflect a wide range of pain mechanisms including neuropathic and nociceptive pain, thus facilitating generalizability of results. For the CTS cohort, 76 patients were recruited from the Department of Neurology and Hand Surgery at the local University Hospital in Oxford, UK. All patients included in the CTS cohort met the electrodiagnostic Bland,  and clinz‐scores in the NSNAP cohort  for data standardization  were excluded. Other exclusion criteria included: diseases involving the nervous system , previous upper limb or spinal surgery, major trauma affecting the upper limb and/or cervical region in the past two years, psychiatric or mental disorders. Thirty‐one healthy, gender and age matched subjects served as controls for the calculation of n = 5, MRI n = 21). Based on the grading system by Pfirrmann et al. , The Ethics Committees Nordwest‐ und Zentralschweiz (EKNZ 2014‐243), the Ethics Committee of the Sir Charles Gairdner Osborne Park Health Care Group (HREC 2013‐096) and the Griffith University Human Research Ethics Committee (#2016/504). All participants gave written informed consent prior to attending a single appointment, during which demographic and clinical parameters (e.g. symptom severity and duration) were measured followed by the performance of both QST and CST. The CST was performed prior to QST. The investigators performing QST were blind to the outcome of CST in the NSNAP and LR cohorts.2.2The QST was performed according to the standardized test protocol developed by the DFNS  were examined with a thermotester . Three repetitions were performed and the average temperature was recorded  and heat pain thresholds (HPT) were evaluated with the thermotester (Somedic Sweden). Patients were tested three times and the mean was used for analysis. Pressure pain threshold (PPT) was tested with a manual algometer (Wagner Instruments) or digital algometer (Somedic). The mean of three repetitions was recorded. Mechanical pain threshold (MPT) was tested as described above. The wind‐up ratio (WUR) evaluates the effect of repeated stimuli, tested with a 256 mN pinprick stimulator. Patients were asked to rate the magnitude of pain evoked by the first stimulus and average of 10 repeated stimuli using a numerical pain rating scale (NRS) from 0  to 100 (worst pain imaginable). The WUR was calculated by dividing the average NRS of 10 stimuli by the NRS of the first stimulus. Dynamic mechanical allodynia (ALL) was tested five times with a standardized brush , a cotton wisp and a Q‐tip. Subjects were asked to rate the pain intensity from 0 to 100 using an NRS as outlined above. The presence of allodynia was determined by any rating above 0.All participants were familiarized with QST procedures on an unaffected control area  before testing the affected pain area. For the CTS cohort, the affected pain area was standardized to the palmar side of the index finger reflecting an area innervated by the median nerve : This set of tests was performed with metal coins  as previously described : MDTCST consisted of two tests, sensitivity to light stroke with a cotton wool and sensitivity to a von Frey filament weighing 16 mN : The VDTCST was evaluated with a tuning fork of 128 Hz frequency. The amplitude of the tuning fork was standardized by releasing the metal fork from a fully approximated position.Vibration detection threshold (VDTCST): The MPTCST was evaluated with two modalities; a toothpick and a von Frey filament weighing 256 mN : For CPTCST, ice cubes were placed in a plastic bag on the patients’ skin for 10 s. For HPTCST, a glass vial filled with hot tap water (40°C) was placed over the skin for 10 s.Cold/heat pain thresholds (CPTCST): The MPTCST for gain of function was evaluated with two modalities: a toothpick and a von Frey filament (256mN) as described for MPTCST loss of function above.Mechanical pain threshold (MPTCST): The PPTCST was evaluated with two modalities; with an eraser mounted on a pencil (7 mm diameter) and with the examiner's thumb. The eraser/thumb was placed over the testing area and pressure was applied for 10 s : The WURCST was established with a toothpick by applying a single stimulus followed by a train of 10 stimuli with the participants rating the ensuing pain on a NRS from 0  to 100 (worst pain imaginable). As per QST protocol, the wind‐up ratio was calculated as the ratio of the two pain ratings.Wind‐up Ratio (WURCST):Dynamic mechanical allodynia  and patients were asked whether or not this produced a painful response.2.3.3For both loss and gain of function CST parameters, patients were asked whether the stimulus applied at the affected site was perceived as increased, decreased or the same intensity compared to the symptom‐free control area. In the loss of function parameters (detection thresholds and MPT) a perception of decreased sensation was interpreted as loss of function. In the gain of function parameters (pain thresholds and WUR), a perception of increased sensation was considered to reflect gain of function. For the gain of function parameters, the intensity of pain was also recorded on a NRS scale ranging from 0  to 10 (worst pain imaginable). Allodynia was rated as present or absent. The NRS score was analysed separately to investigate if the intensity of pain alone could help identify patients with gain of function.2.4z‐scores were calculated using the following formula: z‐score =   or below −1.96 (loss of function) as a clinically relevant sensory dysfunction  to facilitate the calculation of agreement rate as well as the significance and strength of correlation. For QST, a z‐score smaller than −1.96  in loss of function tests and >1.96  in gain of function tests was defined as a sensory dysfunction (positive). For CST, a perceived decreased response on loss of function tests and increased response on gain of function tests compared to the control area was defined as a sensory dysfunction (positive).Quantitative sensory testing parameters except for CPT, HPT and VDT were log‐transformed to achieve normal distribution. The 2.5Data were analyzed for the mixed cohort as well as each cohort separately using SPSS 25 (IBM). Two‐by‐two contingency tables were used to calculate the agreement rate and correlations between QST and CST parameters. Fisher's exact test was used to determine significant correlations between the outcomes of QST and CST in all parameters. In order to assess the strength of correlations between the corresponding QST and CST variables, Phi correlation coefficients were calculated. A Phi coefficient between 0 and 0.1 indicates negligible correlation, 0.1–0.2 weak, 0.2–0.4 moderate, 0.4–0.6 relatively strong association, 0.6–0.8 strong association and >0.8 very strong association between two binary variables  curves to assess if the NRS scores are indicative of gain of function as determined with QST. For those parameters with an area under curve (AUC) exceeding 0.7 indicating acceptable discriminative power  conformed best in the NSNAP cohort  and MPTCST, MPTCSTVF256 in the LR cohort (85%–96%) showed strong agreement  showed a significant correlation ranging from moderate (0.214) to relatively strong (0.420). Loss of function parameters of CDTCST showed a relatively strong correlation with QST in the NSNAP cohort (0.420) and moderate correlation in the mixed cohort (0.249) and WDTCST and MDTCST‐cotton wool were moderately correlated with QST in the mixed cohort . For the CTS and LR cohort, no loss of function parameters correlated significantly with QST measures.Six parameters (CDTCST showed a moderate correlation with its QST counterpart in the CTS cohort (0.311). In the NSNAP and LR cohort, none of the gain of function CST parameters correlated significantly with QST. For the mixed cohort, PPTCSTEraser and PPTCSTThumb demonstrated moderate correlations  in the LR cohort. For example, the MPTCSTVF256 in the LR cohort revealed 96.2% agreement with the corresponding QST parameter, however none of the patients in the LR cohort was classified as positive in the QST. It was therefore not possible to calculate Fisher's exact tests and the strength of correlations. We did not conduct analyses on dynamic mechanical allodynia (ALL) as no patient had allodynia as determined with QST and only three patients with the CST .For several parameters, correlations could not be calculated because of uneven patient distribution Table A‐C: loss3.3CST, HPTCST and PPTCSTEraser in the CTS cohort yielded AUCs of 0.869, 0.766 and 0.797, respectively. The ROC analysis indicated that an NRS value >0.5 in the CPTCST has 100% sensitivity and 68.2% specificity to detect cold hyperalgesia as determined with QST in patients with CTS. The ROC curve of HPTCST and PPTCSTEraser suggested that an NRS value >0.5 has 66.7% and 75% sensitivity and 84.7% and 76.4% specificity to detect heat and pressure hyperalgesia, respectively  were significantly correlated with the outcome of QST, yielding moderate to relatively strong correlations and mostly over 60% agreement rate. Other CST parameters have however not shown significant correlations with the outcome of QST  which may query their value in clinical practice for these patient cohorts. The agreement and correlation of CST parameters with QST varies substantially not only between different parameters, but also among different patient cohorts.Our study investigated the concurrent validity of a comprehensive, low‐cost and time‐efficient CST battery compared to a standardized QST in a cohort of patients with mixed diagnoses. Certain CST parameters . This pattern reflects previously published data, which suggest that loss of function is the predominant phenotype in patients with actual nerve lesions resulting in neuropathic pain such as in our CTS  with traumatic nerve injury. Based on the reported agreement rates ranging from 48% to 58%, the authors concluded that the outcome of bedside sensory testing and QST often differs. Despite including patients with presumably more subtle sensory changes due to non‐traumatic aetiologies, our agreement rates are higher, especially in the NSNAP and LR cohort. In contrast to Leffler and Hansson  revealed comparable agreement rates and correlations between CST and QST as the conservative 1.96 SD cut‐off , we speculated that the pain intensity on an NRS can possibly be used to identify patients with hyperalgesia. However, this was only the case for measurements of CPT4.1This study is an important first step in the development of a low‐cost screening tool to detect somatosensory dysfunction in a clinical setting. However, it would currently not substitute QST, whose quantitative characteristics renders it useful not only as a screening, but also as an outcome measure tool. The identified agreement rates and correlations suggest that parts of the CST are valuable as a clinical screening tool. Future studies are however required to determine whether CST can reliably be used to quantify the identified sensory dysfunctions, so that it can eventually serve as an outcome measure tool.z‐scores is likely to correct for these differences, as they are based on healthy control data collected in comparably sized areas. We cannot exclude that the smaller areas examined during thermal testing in the CTS cohort may have contributed in part to the lower agreement rates especially for the loss of function tests.There are some methodological issues to be considered. Although we included three cohorts of patients representing different pain mechanisms and aetiologies, the sample sizes of these cohorts differed. Although agreement rates were comparable between cohorts, the relatively low number of patients in the NSNAP and LR cohorts may have prevented the identification of significant CST parameters in the ROC analysis. Also, we used the contralateral side as a control side for the CST in all cohorts except for CTS patients, where we used the ipsilateral lateral upper arm. Detection threholds are generally higher over the lateral upper arm than the distal hand (Heldestad Lillieskold & Nordh, z‐scores used for analyses remained unknown to the investigator, as they required additional QST data from healthy participants. Thus at the time of performing QST, the investigator was unaware whether or not participants would be classified as having abnormal z‐scores.A potential limitation in our study is that the investigator performing the QST was blinded to the CST outcome in the LR and NSNAP but not the CTS cohort. However, the QST 5Our study reveals that a subset of tools in the low‐cost CST battery may be valid as a low‐cost screening tool to identify somatosensory dysfunction in a clincial setting, allowing for wider clinical use and more time‐efficient somatosensory phenotyping. However, not all CST tools proved to be useful and there is substantial variability between different patient cohorts, suggesting that certain CST parameters may be most relevant in specific cohorts.All authors have no conflict of interest in the subject matter or materials discussed in this manuscript.BT and AS conceptualized the experiment and acquired funding. GL, HS and KB provided input to the design of the study. KB, AS, BT, LH and SF collected data. GC, AS, BT and CC were involved in data analysis. GC and AS drafted the first version of the manuscript. All authors provided input to the final version of the manuscript. Click here for additional data file. Click here for additional data file.


Clinical laboratories implement a variety of measures to classify somatic sequence variants and identify clinically significant variants to facilitate the implementation of precision medicine. To standardize the interpretation process, the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and College of American Pathologists (CAP) published guidelines for the interpretation and reporting of sequence variants in cancer in 2017. These guidelines classify somatic variants using a four-tiered system with ten criteria. Even with the standardized guidelines, assessing clinical impacts of somatic variants remains to be tedious. Additionally, manual implementation of the guidelines may vary among professionals and may lack reproducibility when the supporting evidence is not documented in a consistent manner.We developed a semi-automated tool called “Variant Interpretation for Cancer” (VIC) to accelerate the interpretation process and minimize individual biases. VIC takes pre-annotated files and automatically classifies sequence variants based on several criteria, with the ability for users to integrate additional evidence to optimize the interpretation on clinical impacts. We evaluated VIC using several publicly available databases and compared with several predictive software programs. We found that VIC is time-efficient and conservative in classifying somatic variants under default settings, especially for variants with strong and/or potential clinical significance. Additionally, we also tested VIC on two cancer-panel sequencing datasets to show its effectiveness in facilitating manual interpretation of somatic variants.https://github.com/HGLab/VIC/.Although VIC cannot replace human reviewers, it will accelerate the interpretation process on somatic variants. VIC can also be customized by clinical laboratories to fit into their analytical pipelines to facilitate the laborious process of somatic variant interpretation. VIC is freely available at The online version of this article (10.1186/s13073-019-0664-4) contains supplementary material, which is available to authorized users. With the rapid development of massively parallel next-generation sequencing (NGS) technologies, a large number of cancer genomes, exomes, or gene panels are being sequenced around the world for both biomedical research and clinical diagnosis. DNA sequencing has become an important component in cancer diagnosis and treatment, which facilitates the implementation of precision medicine. However, determining the clinical impacts of somatic variants in cancer presents a different set of challenges from those for germline variants.https://www.mycancergenome.org), Clinical Interpretations of Variants in Cancer (CIViC) [Various tools and databases have been developed by different laboratories and institutes, in combination with experts’ opinions, for the interpretation of clinical significance on sequence variants. Annotation tools, such as ANNOVAR  and SnpE (CIViC) , OncoKB  (CIViC) , the Pre (CIViC) , and Can (CIViC) . HoweverTo standardize the clinical interpretation of cancer genomes, the Association for Molecular Pathology (AMP), American Society of Clinical Oncology (ASCO), and College of American Pathologists (CAP) published standards and guidelines for the interpretation and reporting of sequence variants in cancer in 2017 . The AMPhttps://cancervariants.org/), but with a strong emphasis on automation of evidence generated from an internal collection of databases and/or a user-defined knowledgebase. It systematically considers seven criteria, including FDA-approved therapies, variant type, population allele frequency, absence/presence in germline and somatic databases, predictive software programs, and pathway involvement to assign scores to each somatic variant and generate the preliminary prediction for clinical impacts. Information suggested by the ClinGen Cancer Somatic Working Group is provided in the final result of VIC, including the allele description, the DNA and protein substitution, the variant type and consequences, and all of the scores of the criteria implemented in the program. This tool assigns somatic variants into four categories: (1) strong clinical significance, (2) potential clinical significance, (3) unknown clinical significance, and (4) benign or likely benign.We have previously developed a variant interpretation framework called InterVar for interpreting clinical significances of germline variants  based onhttps://github.com/HGLab/VIC/).VIC takes either unannotated VCF files or pre-annotated files generated by ANNOVAR as input files. If a VCF file is not annotated, VIC will automatically call ANNOVAR to generate necessary annotations including refGene, ensGene, knownGene, esp6500siv2_all, 1000g2015aug_all, exac03, gnomad211_exome, avsnp150, dbnsfp35a, dbscsnv11, dbnsfp31a_interpro, clinvar_20190305, cosmic89_coding, icgc21, and so on [According to the AMP-ASCO-CAP 2017 guidelines, there are a total of ten types of evidence to predict the clinical significance for somatic variants, including therapies approved by the FDA or reported in professional guidelines, investigational therapies, mutation type, variant allele fraction (mosaic variant frequency (likely somatic), non-mosaic variant frequency ), population databases , germline databases, somatic databases, predictive results of different computational algorithms, pathway involvement, and publications . Among thttps://www.fda.gov/Drugs/ScienceResearch/ucm572698.htm) or included in the professional guidelines (PG) for specific types of tumors such as the National Comprehensive Cancer Network (NCCN) guideline , or act as diagnostic and/or prognostic biomarkers in PG for certain types of tumors; (B) biomarkers based on well-powered studies with experts’ consensus or smaller studies that are repeatedly confirmed or reproduced by different groups; (C) FDA-approved therapies or therapies included in PG for a different tumor type, or investigative therapies with some clinical evidence, or diagnostic and/or prognostic biomarkers with significance based on the results of multiple small studies; (D) biomarkers that have been associated with targeted therapies in preclinical studies with plausible effects, or biomarkers that assist with diagnosing or prognosing diseases themselves or along with other biomarkers on the basis of small studies or some case reports. We complied data from PMKB [The interpretation of somatic variants needs to be focused on their clinical impacts. A variant can be considered as a biomarker for guiding a clinical treatment if it alters the function of a gene or can be targeted by approved or investigational drugs, or predict sensitivity, resistance, and toxicity to a specific drug/therapy. On the basis of the guidelines , the clirom PMKB  and CGI rom PMKB  into ourrom PMKB , then throm PMKB , 15 are The type of mutation, such as the likely loss-of-function (LoF) variants , nonsynoThe variant allele frequency  is used to infer whether a variant comes from somatic cells or inherited from parents when a matched normal sample is not provided.A variant is potentially a germline mutation if the VAF is approximately 50% or 100%. However, certain germline variants, such as large insertions or deletions (INDELs), may cause preferential amplification or capture of normal homologue, resulting in < 50% VAF for germline variants. Laboratories should have clear criteria to differentiate between somatic and germline mutations if a matched normal sample is not available. When a pathogenic germline variant is suspected during tumor-only testing, the variant is recommended to be confirmed with a paired-normal sample according to the AMP-ASCO-CAP guidelines . The labThe frequencies of minor alleles in control populations are useful for assessing clinical significances of somatic variants. Population databases can be used to filter out variants that are deemed polymorphic/benign based on an arbitrary cutoff of minor allele frequency (MAF). Researchers usually use MAF = 0.01 as the cutoff; however, the optimal threshold can vary. It is recommended to assign different cutoff values to various populations/cohorts. If a variant is absent in a large control cohort or present at extremely low frequencies, there could be a piece of evidence for pathogenicity or clinical impact. VIC uses four databases to assess the MAF, including the 1000 Genomes Project , the Exohttps://www.ncbi.nlm.nih.gov/clinvar/docs/clinsig/), assigns a score of 2 for variants with only pathogenic and no benign evidence, a score of 1 for benign or likely benign observations, and a score of 0 for either contradictory cases (both benign and pathogenic reported) or uncertain/unknown significance. The score of this parameter will be compiled with those of other criteria to make the final interpretation.Germline mutation databases, such as ClinVar , are usehttps://www.mycancergenome.org), the International Cancer Genome Consortium (ICGC) [Somatic mutation databases, such as the COSMIC , My Cancm (ICGC) , and Them (ICGC) , containFor missense variants, a variety of tools can predict the likelihood of whether a given variant is damaging to protein function or structure by using evolutionary information, context within the protein sequence, and biochemical properties based on the probabilistic assertions. Most tools are designed for optimal performance on germline variants rather than somatic variants, and the impacts on protein function do not necessarily translate to pathogenicity on human diseases. These computational methods include individual scoring algorithms or systems, such as SIFT , PolyPhehttps://cancer.sanger.ac.uk/census) [https://www.genome.jp/kegg-bin/show_pathway?hsa05200) to measure the involvement of genes in cancer-related pathways. In total, 576 CGC Tier 1 genes classified based on COSMIC v89 were selected to build the “cancer_genes.list” file as one of VIC’s internal databases; these genes have documented cancer-related activities and evidence of mutations in promoting oncogenic transformation. Meanwhile, 1581 genes involved in cancer pathways were retrieved from KEGG, and we organized these genes into the file “cancer_pathways.list.” If a given mutation is located in a critical gene in a cancer-associated pathway (genes in cancers_genes.list), it is marked with a score of 2; if a variant is present in a gene potentially involved in a cancer-associated pathway (genes in cancer_pathways.list), it is marked with a score of 1; as for variants in genes with unclear functions, they are marked with a score of 0. We acknowledge that the gene list and pathway list may not be comprehensive/accurate and may not be optimal when specific types of cancer are examined, and users can substitute their own gene/pathway list in the VIC software.Nonsynonymous mutations in key genes involved in biological pathways will adversely affect the metabolism, signal transduction, or cellular function and consequently may contribute to cancer initiation and progression. Understanding the functions of major genes in a pathway is critical in locating targeted drug-associated mutations. Two internal gene lists were built from the Cancer Gene Census   and the Researchers and clinical professionals publish their research and clinical trial results to share their discoveries with the scientific community. One could study the function and clinical impact of a variant or gene by searching scientific publications. However, the conclusions may vary among studies due to different study designs, methods, populations, and objectives. Because it requires a massive workload to search and filter out the relevant publications, VIC currently does not automatically score a variant based on this criterion. Nevertheless, VIC presents available publications documented in CIViC in the final results (to facilitate human reviewers make judgment); moreover, users can compile a private collection of variants of interest and VIC will take the clinical interpretation defined by users in addition to providing other annotations in the final result. This function can be activated by the “-l user_specified_list” option, and an example of the “known-list file” is provided as Additional file VIC implemented its scoring system based on the seven criteria. Among them, the therapies, mutation type, population data, somatic data, and pathway need to meet the basic criteria while the germline data and predictive software can be optional. The basic scoring system is described in more details in Table VIC is a command-line-driven software program implemented in Java and can be used as a standalone application on a variety of operating systems that support Java. The pre-annotated files generated by ANNOVAR or unannotated files in VCF format or ANNOVAR input format (avinput) can be taken as input files. If the input files are unannotated, VIC will automatically call ANNOVAR to generate the pre-annotated files. Next, VIC takes the seven criteria as the default setting and performs assessment based on internal resources. After the step-one assessment, users could manually adjust each criterion for re-interpretation. In the output file, each variant will be assigned as one of the four tiers following the AMP-ASCO-CAP 2017 guidelines .The analytical procedures and scoring logic of VIC are shown in Fig. EGFR  [https://www.lls.org/leukemia/chronic-myeloid-leukemia/treatment/tyrosine-kinase-inhibitor-tki-therapy), so therapeutic score = 2. (3) It activates the downstream pathway of EGFR and leads to carcinogenesis, so pathway score = 2. (4) It is absent in the 1000G, ExAC, ESP6500, or gnomAD databases; thus, it is likely a rare variant , population database score = 2. (5) It is recorded in the COSMIC, ICGC, and CLINSIG as pathogenic, so somatic databases score = 2. (6) Multiple bioinformatics tools  predict this variant as deleterious, predictive software score = 2. Finally, after manual review, no scores are changed or added, and this variant is classified as strong clinical significance by VIC. Therefore, VIC speeds up variant interpretation by the compilation of information from various sources and the final assignment of four tiers from 10 criteria. In a typical modern computer, it takes about 20 min for VIC to completely interpret 215,000 variants followed by manual review. In comparison, it takes ~2 h for an experienced clinical geneticist to interpret a report on a gene panel containing about 100 variants.We herein illustrated the procedure for the assessment of one variant in a step-by-step fashion. This is a missense variant located in exon 21 of  131550) , which ihttps://cancer.sanger.ac.uk/cosmic/#, version 89, last accessed in June 2019). In total, we retrieved 272,560 unique variants from COSMIC v89 categorized in the following three categories: (1) “pathogenic”, (2) “neutral”, and (3) “none” (no annotation information) by COSMIC. Then, we analyzed these variants using VIC independently.The COSMIC database  is the lFor the “pathogenic” category  in the COSMIC, VIC (automated step) classified 6/14, 783  variants as strong/potential clinical significance, and the rest as uncertain significance  or benign/likely benign . In the “neutral” category of COSMIC , VIC (automated step) classified 416 0.82%) variants as potential clinical significance, 50,026 (99.11%) variants as uncertain significance, and the rest 33 variants as benign/likely benign (0.065%). In addition, for the 48,935 variants without any classification labels in the COSMIC database, VIC (automated step) classified 567 (1.16%) as potential clinical significance and 5 as benign/likely benign (0.01%) and classified the remaining variants  as uncertain significance. In summary, among 272,560 coding variants retrieved from COSMIC, VIC identified 15,772 variants as having strong or potential clinical significance, 256,742 as uncertain significances, and 46 as benign/likely benign  was recorded as “neutral” in COSMIC but rated as “potential clinical significance” by VIC. The differences can be attributed to several reasons: this variant was included in our internal therapeutic database, it was a nonsynonymous SNV in the exonic region of TP53, it was absent in the population databases (extremely low MAF), it was present in ICGC, it was predicted as damaging by seven tools, TP53 was involved in a critical cancer-associated pathway, and there was a “pathogenic” record of this variant in ClinVar. These examples illustrated that VIC uses multiple sources of information to derive a set of evidence to help manual review and interpretation of somatic mutations and ensures reproducibility by documenting the source of the evidence used. It serves a different purpose than typical bioinformatics tools that give in silico prediction on whether a variant is likely to alter protein function.The disagreement between COSMIC and VIC is not surprising due to the lack of confirmed information of many variants. For instance, the mutation c.962C>T located in the coding region of gene https://www.cancergenomeinterpreter.org, last accessed in June 2019) for reclassification of variants with VIC. The four example datasets include annotations of variants in two sets of colorectal cancers, chronic lymphocytic leukemia, NSCLC, and bladder carcinoma. Their catalogues of validated oncogenic mutations aggregate data in the DoCM [The CGI  is a plathe DoCM , ClinVarPTPRU; due to the lack of supportive evidence from the current germline, somatic, and pathway databases, it was categorized into the uncertain group by VIC. As for the validated oncogenic mutations  in CGI, we collected 2543 SNVs out of 2748 somatic variants in the dataset and used VIC to classify their clinical significances. Four variants were categorized by VIC as strong clinical significance and 1121 as potential clinical significances, which is about 44.24% (1125/2543) of the total oncogenic SNV alterations in the CGI’s catalogue, and only one was identified as benign/likely benign by VIC  identified 2 variants with strong clinical significance and 6 variants with potential significance, and all of them were also annotated as known or predicted drivers by CGI. Moreover, VIC (automated step) identified no benign/likely benign variant and 465 variants of uncertain significance, whereas CGI marked none of them as known, 8 as predicted driver, and the rest as not protein-affecting or predicted passenger Table . One of http://www.cbioportal.org/). Datasets of 10 Melanoma and 45 NSCLC cases were downloaded from cBioPortal  for this study. For the melanoma cases (285 variants), we identified 13 variants with strong/potential clinical significances whereas OncoKB annotated 9 of them as oncogenic/likely/predicted oncogenic. For the NSCLC project, VIC interpreted 40 of the 244 mutations as strong/potential clinical significances while OncoKB classifies 38 of them as oncogenic/likely oncogenic/predicted oncogenic  is slightly more conservative in calling variants as having clinical significance than OncoKB; however, manual adjustment based on prior knowledge may change such classifications to a different tier.OncoKB takes into account the information of biological, clinical, and therapeutic resources, FDA labels, NCCN guidelines, selected experts’ recommendations, and the medical literature to provide a four-level evidence classification system to interpret the genomic alterations. We obtained the mutation information of the MSK_IMPACT Clinical Sequencing Cohort project  as well https://civicdb.org/releases, last accessed in June 2019) and assessed by the VIC program. Almost half of the variants retrieved from CIViC were leveled as “C” (350 as case study), among which 5 had strong clinical significance, 174 had potential clinical significance, and 171 had unknown significance based on the results from VIC (automated step). Among the unique SNVs/INDELs, only 4 were predicted as “benign/likely benign,” whereas 13, 291, and 337 variants were identified as “strong”, “potential”, and “uncertain”, respectively, by VIC ). It was listed as level A in the CIViC but predicted as uncertain by VIC, though it is a rare allele in population databases (MAF < 0.005). The reasons are as follows: this variant was absent in our therapeutic and somatic databases, it was interpreted as pathogenic or benign by an equal number of predictive tools, and it was not documented in the genes involved in cancer-related pathways.CIViC is a crowd-sourced and expert-moderated public resource for somatic variants in cancer . It adopIC Table . Among tTo assess the utility of VIC in analyzing real clinical diagnostic dataset, we analyzed the variants of 100 patients with lung cancer using VIC and compared our results with their clinical diagnostic reports. The sequencing panel includes 19 genes associated with targeted therapies on lung cancer and the clinical reports were provided by a diagnostic laboratory in a double-blinded fashion for comparison with VIC. There were a total of 70 SNVs and 26 INDELs reported as clinically significant in the original laboratory reports, among which 69 SNVs and 19 INDELs were classified as strong/potential clinical significance by VIC, demonstrating a 91.67% concordance between VIC and clinical interpretation by experienced molecular pathologists. Our results also showed a much higher concordance rate for SNVs (98.6% consistent) compared to INDELs (73.1%). This is likely due to some INDELs not being fully and/or correctly recorded by databases/resources that VIC has adopted. For instance, a variant in ERBB2  was classified as “uncertain significance” by VIC because there was no relevant information on therapies for this variant in the VIC internal databases. In addition, several bioinformatics tools predicted this variant as benign or uncertain. Similarly, the only contradicting SNV , which is classified as uncertain by VIC but clinically significant by the diagnostic report, lacked the therapeutic information in our databases and consequently was categorized as uncertain. However, by customizing the score of therapy . Among all the 251 somatic variants in the five CHOP samples from panel sequencing data, VIC (automated step) identified 6 variants as strong/potential clinically actionable and 245 as uncertain Table . All sixIn the current study, we describe a strategy to implement the ASCO-AMP-CAP 2017 guidelines and present a computational tool to assess the clinical impacts of somatic variants in a semi-automatic fashion. Similar to the InterVar tool that we have previously developed, the goal of VIC is not to replace human acumen in clinical interpretation, but rather to facilitate automatic generation of evidence codes and provide a final summary of the results using evidence codes. We caution that VIC is not designed as a robot with artificial intelligence that gives clinical interpretation automatically, but as a tool to facilitate human beings make clinical judgements. Our comparative analysis with several clinical databases demonstrated that VIC can be utilized in clinical settings to facilitate the somatic variant interpretation process.We applied VIC to annotate and interpret the variants in COSMIC, CGI, OncoKB, and CIViC, and observed that a small proportion of the variants were classified as strong/potential clinical significances by both VIC and one of the public data resources. This may be due to several reasons. For example, public databases such as COSMIC contain the clinical significance of variants provided by various submitters and some may be simply retrieved from literature without careful curation. Sometimes different scientists use different criteria for assessing the clinical impacts, which leads to different results for a variant. Unsurprisingly, a proportion of variants in these databases might be inaccurately marked as “pathogenic” and are in fact not pathogenic or not related to cancer progression and treatment. On the other hand, it is also possible that some variants classified as tier 3/4 by VIC could be changed to tier 2  if additional evidence-based information is provided by human input : Platform independentProgramming language: JavaOther requirements: Java 1.8 or higherLicense: GNU GPLAny restrictions to use by non-academics: license neededAdditional file 1:An example of customized evidence file. (TXT 1 kb)Additional file 2:An example of the “known-list file”. (TXT 283 bytes)Additional file 3:More details of the scoring system in VIC. (DOCX 14 kb)


Kerria chinensis at different developmental stages and identified lac secretion-minimum and lac secretion-active stages of the insect. We then analyzed transcriptomes of lac secretion-minimum and lac secretion-active stages of the insect. Based on expression profiles of genes in different stages of the insect, we identified pathways and genes that are potentially involved in lac synthesis and secretion in K. chinensis. Our study lays a foundation for future studies to reveal the molecular mechanisms and pathways of lac synthesis and secretion in this beneficial insect.Lac is a type of natural resin secreted by lac insects and is widely used in the military and other industries because of its excellent adhesion and insulation properties. The main ingredients of lac are lactones and lactides, which are formed from hydroxy fatty acids and sesquiterpene esters. In this study, we measured lac secretion rates by the insect  Kerria (Hemiptera: Kerriidae) [Lac is a mixture of natural resins produced in and secreted by insects from the genius rriidae) . The userriidae) . Due to rriidae) ,3.The composition of lac derived from different insect species varies. Host trees and environmental conditions can also affect the chemical composition of lac secreted even from the same insect species. In general, lac is mainly composed of lac resins, shellac waxes, laccaic acids, and other organic chemicals . Lac resThe biosynthetic pathway for polyterpene esters is the mevalonate pathway (MVA) in the cytoplasm ,5. In thKerria contains several species that are capable of secreting lac, including Kerria chinensis, K. lacca, K. yunnanesis, K. sindica, K. ruralis, K. pusana, and K. nepalensis. Different species are distributed in different regions, including Thailand, Myanmar, India, and China. One of the main species for lac secretion in South China is K. chinensis [K. chinensis is an insect with distinct sexual dimorphism. Males and females are not only remarkably different in morphology and life cycle  to generate transcriptomes with samples from different developmental stages of the insect for gene identification; and (2) to initially identify genes and pathways that are potentially involved in lac synthesis and secretion based on the transcriptomic data.In recent years, high-throughput sequencing technology has been widely used in gene mining and functional identification of various organisms. Insect transcriptome research has yielded fruitful results ,14, suchhinensis ,16,17. HK. chinensis was reared on Schleichera oleosa in the experimental station of Yuanjiang, Research Institute of Resource Insects, Yunnan province, China . The female lac insects from larval stage (L), early adult stage (A1), and mid–late adult stage (A2) were collected and used for RNA extraction, library construction, and sequencing. Similar samples were also prepared for quantitative PCR analysis, and for individual assays to determine lac secretion rates.K. chinensis were cut and harvested as sticklac. The harvested sticklac was soaked in 95% alcohol until lac was completely dissolved into the alcohol solution, which was then filtered to separate insects and lac. Individual insects were counted and weighed. The weight of crude lac was derived by subtracting the weight of insect bodies from the sticklac weight. The average crude lac secreted per insect was obtained through dividing total crude lac by the number of insects. The rate of individual secretion was obtained through dividing the difference between crude lac secretion on the initial collection date and crude lac secretion on the final collection date by the number of days. Three different stage female samples were measured, and three independent biological replicates were conducted for each measurement.Branches of host plants colonized densely with The average amount of lac secreted by individual insects was calculated by Formula (1), whereas the individual secretion rate was calculated by Formula (2).M represents the amount of individual secretion of the lac insects; m1 represents the total weight of lac and insects in a lac block (containing lac and insects); m2 represents the weight of insects in the lac block; and N represents the number of insects in the lac block.In Formula (1), v represents the individual secretion rate; nM represents the amount of individual secretion in the nth time; Mn+1)th time. nT represents the nth acquisition time; and Tn+1)( represents the (n+1)th acquisition time.In the Formula (2), ® spectrophotometer , and a Qubit® RNA Assay Kit  in a Qubit® 2.0 Flurometer , and an RNA Nano 6000 Assay Kit  on an Agilent Bioanalyzer 2100 system , respectively. A total amount of 3 μg RNA per sample was used for mRNA enrichment using magnetic beads with Oligo (dT). mRNA species were fragmented into short pieces with average size 150~200 bp. The fragmented mRNA pieces were used as templates for reverse-transcription with random hexamer primers and M-MuLV Reverse Transcriptase (RNaseH)- to synthesize the first strand cDNA. Second strand cDNA was synthesized subsequently using DNA Polymerase I  and RNase H . Remaining overhangs were converted into blunt ends via exonuclease/polymerase of polymerase I . After adenylation of 3′ends of the DNA fragments, AMPure XP beads were used for fragment size selection. cDNA libraries were obtained by PCR amplification, and the PCR products were purified by AMPure XP beads. Quality of the libraries was measured by Qubit2.0, Agilent2100, and q-PCR. After cluster generation, the libraries were sequenced on an Illumina HiseqTM2500 platform, and paired-end reads were generated.Total RNA from L, A1, and A2 stages was extracted using Trizol Reagent  according to the method provided by the manufacturer. RNA quality was monitored on 1% agarose gels. RNA quantification and integrity were checked using a NanoPhotometerAfter sequencing, raw reads were subjected to credibility analysis. Sequences with a linker, sequences with N greater than 10%, and low-quality sequences  were removed and discarded, and the remaining reads were referred as Cleans Reads. Q20, Q30, GC-content, and sequence duplication levels of the clean reads were calculated. All the downstream analyses were based on clean reads data. Transcriptome assembly was accomplished based on the de novo splicing of valid sequences . The traAssembled unigenes were annotated by blasting against the NCBI non-redundant protein (Nr) database, NCBI nucleotide database (Nt), Swiss-Prot protein database (Swiss-Prot), Protein families database (PFAM), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, Clusters of Orthologous Groups (COGs) database, and Gene Ontology (GO) database ,20. The K. chinensis transcriptome data to perform qPCR experiments. The list of gene-specific primers is shown in −ΔΔCT method [To validate the results of RNA-seq analysis, several genes were selected from T method . Actin wRead counts were used to estimate the levels of gene expression. In order to reduce false positives, trimmed mean of M values (TMM) was used to standardize data processing. Differentially expressed genes were identified by DEGseq with q value < 0.005 and log2 (fold change) > 1 ,24.http://www.ncbi.nlm.nih.gov/sra/); the accession for our submission is PRJNA489372.We have deposited our data in the Sequence Read Archive (SRA)  to 32 KEGG pathways after removing unigenes associated with the human diseases. Among the classified unigenes, 272 were annotated to ‘metabolism of terpenoids and polyketides pathway’, include 142 unigenes involved in ‘terpenoid backbone biosynthesis (PATHWAY: ko00900)’, 10 unigenes involved in ‘sesquiterpenoid and triterpenoid biosynthesis (PATHWAY: ko00909)’, one unigene involved in ‘monoterpenoid biosynthesis (PATHWAY: ko00902)’, one unigene involved in ‘diterpenoid biosynthesis (PATHWAY: ko00904)’ . In addiThere were 2774 genes expressed differentially between A1 and A2, with 990 genes up-regulated and 1284 down-regulated. There were 2331 genes expressed differentially between A2 and L, with 706 genes up-regulated and 1625 down-regulated. There were 1330 genes expressed differentially between A1 vs. L, with 937 genes up-regulated and 393 down-regulated A. CombinAs shown in The second largest category of genes that were commonly down-regulated was ‘protein metabolism’, which included proteins involved in protein synthesis, folding, modification, and degradation . There wOverall, metabolism via central metabolic pathways was reduced in insects at lac secretion-active stages based on the proportion of genes that are commonly up- or down-regulated . There wIn contrast to the overall reduction in protein metabolism and unevenly affected central metabolic pathways, the metabolic pathways of fatty acids, lipids, and terpenes were shifted in the lac secretion-active stages in comparison with the lac secretion-minimum stage . There wFS-1 , FS-8 , FS-1 , FS-2 (acyl-CoA synthetase), FS-7 (acyl-CoA synthetase), FS-5 (fatty acid synthase), FS-11 (fatty acid synthase), FS-12 (fatty acid synthase), FS-6 (fatty acid synthase), FS-10 , FS-3 , FS-4 (fatty acid desaturase), FS-14 (fatty acid desaturase), FS-13 (fatty acid desaturase), FS-18 (fatty acyl-CoA elongase), FS-17 , FS-9 (17-beta hydroxysteroid dehydrogenase), FS-16 , and FS-15 (enoyl-CoA hydratase); genes involved in terpenoid biosynthesis: TBS-4 (hydroxymethylglutaryl-CoA synthase), TBS-2 (polyprenyl synthetase), TBS-1 (polyprenyl synthetase), and TBS-3 (decaprenyl-diphosphate synthase); and genes involved in UDP-driving glycosylation: UDP-1 , UDP-2 , UDP-3 (UDP-glucuronosyl/-glucosyl transferase), UDP-4 (UDP-glucuronosyl/-glucosyl transferase), and UDP-5 (UDP-glucuronosyl/-glucosyl transferase). As shown in FS-1, FS-3, FS-4, FS-6, FS-9, FS-11, FS-13, FS-14, FS-15, FS-16, FS-17, FS-19, TBS-3, and TBS-4, were confirmed by qPCR , which will provide a foundation for screening the follow-up genes associated with lac biosynthesis.The secretion rate of lac was different between different individuals. The secretion rate of e stages . At larve stages . ThroughK. chinensis (K. chinensis.Based on our results, we propose a hypothesis for lac synthesis in hinensis . The modKerria chinensis as the object of study of lac secretion in different periods were measured to reveal the lac secretion characteristics and dynamics. By analyzing the transcriptomes of different stages of K. chinensis, we speculated the correlation of gene functions through the link of gene expression level and lac secretion rate; the results showed that 28 candidate genes that may be involved in lac synthesis were preliminarily screened; meanwhile, the results provided a scientific basis for the verification of molecular mechanisms related to lac secretion.In this paper, the amount of individual secretion and rate of


Children admitted to nutritional rehabilitation units (NRUs) for inpatient treatment of severe acute malnutrition (SAM) are at high risk of poor developmental and nutritional outcomes. The Kusamala Program is an interactive hospital-based counseling program for primary caregivers of children with SAM that integrates three modules: nutrition and feeding; water, sanitation, and hygiene (WASH); and psychosocial stimulation. This mixed methods feasibility study aimed to evaluate the implementation of the Kusamala Program in an NRU setting and developmental outcomes in children with SAM 6 months after inpatient treatment.An internal pilot trial including the first 30 children and their primary caregivers enrolled to a cluster-randomized controlled trial of the Kusamala Program was performed. Barriers and enablers were identified in a qualitative study with a focus group discussion (FGD) and in-depth interviews (IDIs) with 12 hospital staff members, including five NRU nurses who deliver the Kusamala Program.Results demonstrated high participant engagement (100%) and adherence (87%) of primary caregivers to the Kusamala Program. Potential barriers to implementation identified through the qualitative study were caregivers’ perceived value of the program, prioritization of other ward activities, and shortages of staff. On the other hand, enablers to implementation were engaging other staff members, motivation and work ethic, and refresher training.This mixed methods study demonstrates the feasibility of implementing the Kusamala Program in a real NRU setting. The full cluster-randomized controlled trial will be completed to evaluate the effectiveness of the Kusamala Program.NCT03072433. Registered on 7 March 2017—retrospectively registeredClinicalTrials.gov, The online version of this article (10.1186/s40814-018-0347-8) contains supplementary material, which is available to authorized users. An estimated 250 million children in low- and middle-income countries around the world are unlikely to reach their developmental potential in terms of cognitive, language, socioemotional, and motor function –3. ThereImportant contributors to poor child development include malnutrition itself, acute illnesses and infection, and inadequate psychosocial stimulation . TherefoMalawi is one of 34 countries contributing to all but 10% of the global prevalence of malnutrition . Anthropz-scores compared to a reference population of Malawian children [A cross-sectional study done at an NRU in Malawi known as the Moyo NRU used the Malawi Developmental Assessment Tool (MDAT) to evaluate development in children at discharge from inpatient treatment for SAM , 18. Thechildren , 18. A schildren . These fchildren , 20, 21.A systematic review evaluating the evidence of psychosocial stimulation interventions for children with SAM indicated that based on two published studies in this population, there are positive impacts of this type of intervention on child development –24. ThisThe Kusamala Program is an interactive counseling program that was designed by researchers, nurses, and clinicians for primary caregivers of children with SAM with the goal of improving developmental and nutritional outcomes . It is aIn order to quantitatively assess the effectiveness of the Kusamala Program in children hospitalized with SAM, a protocol for a pragmatic cluster-randomized controlled trial was designed NCT03072433) [72433 [30To determine engagement and adherence rates of participants to the Kusamala ProgramTo obtain data of developmental outcomes of children after hospitalization with SAM to re-estimate the sample size for the full cluster-randomized controlled trialTo gain insight about potential barriers and enablers to implementation of the Kusamala ProgramThe objectives of this mixed methods feasibility study were:This mixed methods feasibility study was done at the Moyo NRU at the Queen Elizabeth Central Hospital in Blantyre, Malawi. The internal pilot trial followed the same parallel design as the full cluster-randomized controlled trial and included the first 30 trial participants, which is standard for pilot studies , 35.The Good Reporting of A Mixed Methods Study framework was followed throughout this paper, in addition to the Consolidated Standards of Reporting Trials (CONSORT) 2010 statement with extension to randomized pilot trials  present at hospitalInclusion criteria:Primary caregiver declined to give informed consentChild with a known terminal illnessChild requiring a surgical procedureExclusion criteria:Primary caregivers gave written informed consent on behalf of themselves and their children. For primary caregivers who were illiterate or unable to write, a thumbprint was taken in the presence of an impartial witness. Pre-defined detailed information about the qualitative study was provided to potential informants, and they were given time to make an informed decision. Written or verbal records of the consent were obtained before the FGD and IDIs took place. All potential informants were made aware that participation is voluntary.IDIs were conducted in June and July 2017, following the completion of recruitment of the internal pilot trial and once all children in the internal pilot trial were discharged from hospital. The FGD was completed in February 2018 after NRU nurses had been delivering the Kusamala Program for more than 1 year giving them enough experience to provide detailed information on delivery, implementation, and sustainability. The training provided to these nurses for the Kusamala Program has been presented in the study protocol . Action The IDIs followed a purposive sampling approach in which NRU staff members, including two field workers, one ward clerk, and two patient attendants, and the two core nurses at a neighboring pediatric ward at the Queen Elizabeth Central Hospital were identified. No eligible informants declined to participate. The FGD included the five permanent NRU nurses, all of whom were trained to conduct the Kusamala Program. One of the NRU nurses participated as both an informant and a leader of the FGD to encourage discussion with NRU nurses. Two local female enumerators fluent in English and Chichewa were also involved in completing, transcribing, and translating the FGD and IDIs, respectively. Both had prior experience in qualitative research and were individuals that informants had a trusting relationship with, allowing for credibility of the data. Informants were advised that enumerators’ goals were to confidentially obtain positive and negative information and that varying opinions were expected and encouraged. They were also notified that information that they shared would not impact their employment in any way. The enumerators kept notes during the data collection process to reflect on whether there were any potential biases that could influence the relationships with informants.A computer-generated randomization scheme for each cluster at a ratio of one intervention week to one comparison week was used as described before. For the internal pilot trial, the ratio was not pre-specified as it simply included the first randomized clusters from the full trial .Nurses facilitated the Kusamala Program in the play room in back bay of the NRU. This included four sessions over 4 days, beginning at around 3:15 PM, each involving 45 min of counseling of primary caregivers during which nurses provided information and facilitated discussions, followed by 45 min of interactive play supervised by nurses. Content of the Kusamala Program is described in further detail in the study protocol . PrimaryAside from medical and nutritional therapy at the NRU, the standard of care included access to the play room in the NRU during weekday afternoons. Nurses told primary caregivers in the comparison clusters to use the same area where toys are available, but nurses did not facilitate interactive play sessions. This also acted as a means of blinding study personnel and participants to intervention delivery. When children were discharged from hospital, nurses or other NRU staff members counseled primary caregivers on basic nutrition and feeding and WASH messages as is standard practice. No further follow-up counseling was done as a component of this trial.z-scores, length- or height-for-age z-scores, and weight-for-age z-scores per WHO standards [Data was collected upon enrollment, at discharge from hospital, and 6 months following discharge at the homes of participants. Baseline data collected included household information and primary caregiver characteristics as well as child characteristics including nutritional status assessed by anthropometry. Anthropometric measures included bilateral pitting edema, mid-upper arm circumference, weight-for-length or weight-for-height tandards , 42.The primary outcome measure for the full cluster-randomized controlled trial is the MDAT . This deParticipant engagement was measured by the proportion of primary caregivers enrolled to the intervention clusters who attended day 1 of the Kusamala Program; participant adherence was the proportion who attended all 4 days of the program and received a certificate of completion.A semi-structured guide was created for the FGD and first included discussion questions followed by participatory activities. The first activity was problem identification and solving, in which informants, all of whom were nurses involved in delivering the Kusamala Program, were asked to identify actual or hypothetical problems around implementing the Kusamala Program. Another activity was to invite informants to each recall one issue, success, or unique moment encountered while delivering the Kusamala Program. The final activity was for each informant to imagine going to another NRU and convincing others to introduce the Kusamala Program. This FGD took place over two 3-h sessions in a private room led by one enumerator and the assigned NRU nurse, with support from a research assistant who audio-recorded the FGD and took detailed notes. The FGD was conducted in English since all NRU nurses are proficient and was transcribed verbatim.An IDI guide with open-ended questions was designed and pilot-tested with a Chichewa-speaking staff member working in the NRU. The interview guide was designed to allow for the enumerator to use prompts and probes. Interviews were done one-on-one in a private setting in the NRU or an office nearby, allowing for informants to share information openly. The duration of each interview was between 15 and 30 min. Interviews were audio-recorded, transcribed verbatim, and translated back to English if done in Chichewa.z-score calculations [z-scores standardized by age were calculated based on a reference population of children in Malawi [z-scores of the four domains at follow-up.Data for the internal pilot trial was double-entered into a Research Electronic Data Capture (REDCap) database and into WHO Anthro v3.2.2 , 44. Disulations , 45. Desn Malawi . The samNVivo 11 was used to analyze qualitative data from the FGD and IDIs . DerivatEthical approval for the cluster-randomized controlled trial was obtained from the University of Malawi College of Medicine Research and Ethics Committee and the Hospital for Sick Children Research Ethics Board. For the qualitative component, ethical approval was obtained from the University of Malawi College of Medicine Research and Ethics Committee and the Liverpool School of Tropical Medicine Research Ethics Committee.A total of 13 clusters were included in the internal pilot trial to include the required first 30 participants enrolled to the trial. Of the 36 eligible primary caregivers, 30 were enrolled meaning that the acceptance rate was 83.3%. One primary caregiver in the intervention arm withdrew from the study.Baseline characteristics and nutritional status of children are summarized in Table Participant engagement, defined as the proportion of primary caregivers enrolled to intervention clusters that attended day 1 of the Kusamala Program, was 15/15 (100%). Of these, 13/15 (86.7%) attended all 4 days. Two primary caregivers did not attend all 4 days, as their children were discharged prior to completion of the four Kusamala Program sessions. Therefore, 13/13 (100%) of participants remaining in-hospital for the course of the 4-day intervention were adherent.z-scores were computed for 12 children in comparison clusters and 14 in intervention clusters at discharge, and 11 children in each arm at follow-up. One child in a comparison cluster was excluded from the analysis of MDAT z-scores because of a pre-existing neurodisability. MDAT z-scores for gross motor, fine motor, language, and social domains at discharge and follow-up are displayed in Fig. MDAT z-score SD was calculated to be 1.55. An effect size of 0.5 was used to represent a potentially clinically significant change in MDAT z-scores to justify the implementation of the Kusamala Program. Cluster sizes from this internal pilot trial were small for the calculation of an intracluster correlation coefficient, and therefore a value of 0.05 was selected based on the postulation that developmental outcomes will not vary greatly by cluster. Using α of 0.05 and 80% power, an estimated minimum of 158 children per arm (N = 316) should be included in the analysis of the full trial [N = 320) in the original study protocol, which will be followed since it is higher than the recalculated sample size [N = 400) should be included in the full trial.From the 22 children successfully followed up, the average MDAT ll trial . This nuple size . To accoThe three main barriers to implementation of the Kusamala Program included caregivers’ perceived value of the Kusamala Program, prioritization of other ward activities, and shortages of staff.One of the children was a cerebral palsy patient. To the caregiver, the talk of play and stimulation was not applicable. I should bring the message in a different way that this caregiver should understand. After counselling, she understood, and you could tell with how she was playing with her child. The mother was understanding of how important it is compared to the perceptions she had at the beginning. What I have seen is that some take the program as normal but for some because of their beliefs and misunderstanding of the program as well as religious beliefs, it is difficult to welcome the program, but others do welcome it well without any problem. It was raised in two IDIs and in the FGD that some caregivers did not understand the purpose of the program before beginning the intervention and were therefore less willing to participate. This was often because caregivers were preoccupied by their child’s illnesses or conditions such as cerebral palsy. In the FGD, nurses discussed how to approach primary caregivers to ensure that they understand the Kusamala Program before making their decision to participate.Especially when you are in the ward and you need to start the intervention, when you’d like to start to teach the caregivers, you have got other things to do and you don’t always start on time. The other barriers included prioritization of other ward activities, identified by two informants in IDIs, and shortages of staff, described by three participants in IDIs. Both of these barriers were also discussed by nurses in the FGD.In these wards, there are those we call domestic staff, so it starts from cleaners, patient attendants, so such people are the ones that would do that as nurses are always busy and they cannot do that but the patient attendant, ward clerk, cleaners are the ones who can do this or help in this. When you look at the setting of the Moyo NRU itself it sometimes is not busy, sometimes is a very busy ward. So, let’s say there are three or four nurses who work on shifts. You can’t have all of them at the same time and the ward is busy. I think it would be very ideal to actually have someone outside of that. Acting as a potential solution to these barriers, four informants participating in IDIs felt that others working in the NRU aside from nurses could be involved in delivering the Kusamala Program. They explained that other staff members such as cleaners, patient attendants, or ward clerks could be trained to deliver the Kusamala Program and that this would allow for the nurses to conduct other work.What made it to work so well is mainly from the fact that the ones who are involved in this program work very hard. Perhaps there is a need for refresher training more frequently so that people should be well updated and acquire new knowledge and skills to support the patients. Nurses in the FGD and two IDIs recognized motivation and work ethic as an additional enabler of the Kusamala Program. Linked to this enabler was the inclusion of refresher training to maintain this motivation and increase knowledge, which was mentioned by nurses in the FGD and four informants that completed IDIs.This feasibility study applied a mixed methods approach, first with an internal pilot cluster-randomized controlled trial evaluating child development and implementation outcomes. Exploratory qualitative methods through a FGD and IDIs were then utilized to gain insight about potential barriers and enablers to implementation of the Kusamala Program from the perspectives of personnel involved in delivering the Kusamala Program.A systematic review of studies evaluating interventions to improve child development showed that implementation outcomes are rarely described . This isz-scores from this internal pilot trial were used to recalculate the sample size for the full trial, as no other studies have examined follow-up MDAT z-scores of children after inpatient treatment of SAM. Because the recalculated sample size estimate was lower than the estimate in the protocol, the original estimate will be accepted [Child development was assessed using the MDAT, a culturally appropriate tool to evaluate four domains of development. MDAT accepted , 34.Adjustments to the full cluster-randomized controlled trial and the Kusamala Program were made according to results of this feasibility study. Based on the potential barrier of prioritization of other ward activities which could interfere with starting the sessions on time, a fidelity assessment of the Kusamala Program was added to the full trial. An enumerator trained in early child development will assess the delivery of 20% of intervention sessions with an observational assessment tool based on the WHO Care for Child Development Package and an overall rating of the delivery of each session using a Likert scale . The enuAnother addition was a 3-day training session for NRU nurses in early child development after completing recruitment of the internal pilot trial. Although all nurses have been previously trained on counseling components of the Kusamala Program, a more comprehensive overview of child development will better equip them to conduct play sessions. Child development training is becoming widely available in Malawi and other low- and middle-income countries through efforts such as UNICEF’s Early Child Development initiatives and therefore could be accessible for other NRU staff members , 48.The Kusamala Program was also shortened to last 75 min per session, totaling 5 h per week, as it was impractical for NRU nurses to have 90-min sessions per week day in addition to their normal ward duties which are outlined in Additional file One limitation of the internal pilot trial is that enrollment was lower than anticipated. This could be attributed to the scale-up of community-based efforts to manage malnutrition. Therefore, the average and median cluster sizes of 2.6 and 2.0 participants, respectively, were smaller than expected. This meant that an intracluster correlation coefficient was not calculated from the internal pilot study data for the recalculation of the sample size.There were also few eligible informants for FGD and IDIs as there are limited core staff members, which is a characteristic of a low-resource setting. This meant that it was not possible to reach data saturation. However, the aim was to collect detailed information from those who are most familiar with the NRU and who are key to the implementation of interventions like the Kusamala Program in real NRU settings.The Kusamala Program for primary caregivers of children with SAM was evaluated in an NRU setting in Malawi using a mixed methods approach. High engagement and adherence of primary caregivers to the Kusamala Program was achieved within an internal pilot trial. The Kusamala Program is feasible to implement upon addressing barriers and strengthening enablers.Additional file 1:CONSORT 2010 checklist for randomized pilot and feasibility trials. (DOCX 21 kb)Additional file 2:Moyo NRU daily schedule and timing of the Kusamala Program. (DOCX 14 kb)Additional file 3:The dataset supporting the conclusions of this article. (XLS 116 kb)


PLOS ONE with the publication of Gietel-Basten et al. (2019). The core question concerns how much higher China’s birth rates might have been had birth quotas not been enacted and enforced. Gietel-Basten et al. argue that the selection of such comparators in recent studies may reflect subjective choices. They profess to avoid such subjectivities by using what they present to be a more scientific, objective, and transparent statistical approach that calculates a weighted average of birth rates of countries with other characteristics similar to China’s. Yet the authors make subjective choices regarding the non-fertility characteristics used to form their comparators which leads to an underestimation of the impact of birth planning. Moreover, their visual presentation, which focuses on the two key sub-phases of the birth program, underrepresents its overall impact. Their comparators suggest that China’s population today would be just 15 million more had it not enacted any birth restrictions since 1970 (one percent above its current population) and that in the absence of one-child limits, which began in 1979, China’s population would be 70 million less. At the same time, the authors acknowledge that the one-child program has had numerous negative consequences. It seems fair to ask how such consequences could result if the program had no significant impact on childbearing decisions.For nearly half a century, parents in China have faced compulsory quotas allowing them to have no more than one or two children. A great debate in recent years over the impact of this program on China’s population continues in  Demography, I estimated that had China not imposed birth restrictions, its population would now be well more than half a billion larger and that by 2060 its population would be one billion larger [1]. These estimates were based on an alternate model of China’s fertility decline proposed by colleagues drawn from 16 countries with fertility rates close to China’s in 1970 [3]. The publication of the article was immediately followed by objections to my findings that China’s official estimates of the impact of this program were plausible, findings that were at odds with claims by other observers [5].China began in 1970 to control the size of its population through compulsory birth quotas, an effort it saw as essential to its rise (and restoration) as a world power. The demographic impact of this now half-century implementation of one- or two-child quotas has inspired considerable debate. In a 2017 article published by 6] continues the debate on this subject. The first author, a co-author of an earlier commentary [5], is now joined by two new authors to present a numerical exercise to assess the impact of China’s birth program. They claim that their advanced statistical methods provide a more scientific and objective way of constructing comparators for what China’s birth rates would have been in the absence of policy intervention. In this comment, I contend that the authors made subjective decisions in setting up their comparators which lead to an underestimation of the impact of the birth planning program. Moreover, their visual presentation of results, which focuses on the marginal impacts of the two major sub-phases of the program, provides an incomplete assessment of the program’s overall impact. Gietel-Basten et al. conclude that the one-child policy phase of the birth program (1980–2015) did “not have a statistically significant impact,” an apparent erasure of the impact of that program from history.The publication of Gietel-Basten et al. [8]. It refers to the strictest five-year phase (1980–1984) of a broader half-century program since 1970 designed to control China’s population. During the other 45 years, at least half of parents  were allowed to have two children, or even more, given a variety of exemptions . What makes this program so unique is that, unlike the family planning programs of other countries designed to help parents have the number of children they wanted, China imposed a birth planning program under which the government specified how many they could have [12].Most everyone has heard of China’s “one-child policy.” And that may be part of the problem, because this phrase is a misnomer [5], to their credit, they start by evaluating the impact of the two-child program during the 1970s prior to one-child restrictions.Much of the debate in this line of research reflects professional differences of opinion about which era deserves evaluation. In my view, such evaluations should begin by considering the impact of the entire half century of birth planning, which has included both one- and two-child quotas as well as evolving enforcements of such quotas. Although the title of Gietel-Basten et al. focuses on “the one-child policy” era during which some portion of parents faced one-child limits (1980–2015)  may reflect subjective choices. They offer the synthetic cohort approach as a better method for choosing comparators for China’s birth rates. The procedure requires the identification of characteristics other than fertility which, after application of their statistical model, will determine which countries are most similar to China during a given time period. Based on the resulting weights for each country, the synthetic comparator will be the weighted averages of the TFRs for those countries fixed in each year forward and backward in time. The authors then rely on a pair of population projections (based on the TFRs for China and the synthetic comparator) to determine how much higher China’s population would have been without policy intervention.Gietel-Basten et al. suggest that estimates in studies which select such counterfactual birth rates based on a single country or a basket of countries . That program promoted later marriage, longer birth intervals, and fewer births, with quotas of two births in some areas (and quotas of three births in others). However, as later pointed out by Gietel-Basten et al. [6], the policy restrictions under that program began at least two years earlier. In fact, China broke with Marxist precepts around 1970 when Premier Zhou Enlai specified maximum population growth targets for 1975 [12]. By delaying the start of program until 1973, the authors estimate of China’s population reduction in the 1970s due to that shock (85 million) is too low.First, the initial birth policy shock considered by the authors is said by them to date from 1973, the year when the dominant policy during the 1970s was formally dubbed Second, Gietel-Basten et al. made subjective choices regarding which non-fertility variables to include in constructing their synthetic cohorts. Although GDP per capita and average years of schooling seem reasonable choices, their decision to include average childbearing age is questionable, given that this variable is so closely correlated with fertility rates (the variable which the synthetic method will construct). Their inclusion of the sex ratio at ages 0–4 is also questionable. The authors note that sex ratio imbalances among young adults may be relevant for adversely affecting the likelihood of marriage for the sex in greater supply . Although that may indeed be true, one wonders why they chose to use instead sex ratios at ages 0–4, an age grouping at least twenty years below the typical age at marriage.Third, given the lack of data in many countries for the non-fertility variables they chose, the number of countries available for their synthetic cohort analysis was greatly reduced. The original dataset included 187 countries for which fertility estimates were available, but that number shrank to only 64 once all the countries with missing data were excluded. The supplementary tables reveal that the 64 remaining countries had TFRs on average more than a full birth lower than the 123 countries that were excluded from the analysis (see below). The exclusion of countries with higher fertility (which tend to have lower incomes), forces their model to construct comparators for China from countries with lower fertility and higher incomes. Indeed, Table 1 of Gietel-Basten et al. confirms that the average income levels of the 64 countries available for their analysis is considerably higher than that which China had during comparable periods. None of the robustness checks presented by the authors address the biases that result from the exclusion of 123 countries from their analysis.Fourth, the country weightings themselves raise concerns. Although one would expect variation in the weighted composition of countries at different points in time, the weights they show are unstable. Consider the highest weighted countries for the three successive policy periods examined:Pre-1973 (which they use to estimate the impact of the two-child era of the 1970s):India 36.9%, Jordan 21.1%, Thailand 15.2% (their Table 2)For the period 1973–1979 (which they use to estimate the impact of the one-child era starting in 1979):South Korea 75.2%, Thailand 16.0%, India 4.1% (their Table 2)For the period 1980–1991 (to estimate the impact of another policy tightening in 1991):Greece, 68.3%, India, 31.2% For instance, India, which accounts for more than 30 percent of the weighted average in the first and third eras, only constitutes 4 percent in the second era.More worrisome is how three quarters of the comparator for the period 1973–1979 (used to gauge the impact of the subsequent one-child era) could be drawn from South Korea alone, a country that was experiencing perhaps the most rapid economic development of any country in the world around that time, at least two decades ahead of China. South Korea also experienced one of the world’s most rapid fertility declines and currently has the world’s lowest birth rates. For every year of the resulting series  be if it had not implemented its birth limitation program at all (neither two-child nor one-child quotas nor any related enforcements)? Second, how much higher would China’s fertility (and population) be if it had implemented just the two-child program in the 1970s and thereafter maintained that program rather than tightening to one-child quotas around 1979/1980? Yet the first  question is not adequately addressed in their discussion or the accompanying graphics, and because of that their answer to the second is debatable.To their credit, Gietel-Basten et al. [1].The dashed line in their 1], their synthetic comparator appears to suggest that China’s population today is at least 700 million lower due to the birth planning program, half of its current population.Thereafter, however, the authors cut off the dashed line in 1979 as well as the pink-shaded section below it. If the authors looked to answer their first question regarding the impact of the overall birth program, there appears to be no justification for such truncation. The dashed line could have been easily extended beyond 1979 based on the country weights derived from their “pre-1973” synthetic cohort analysis. Indeed, they include a graphic showing that very extension in an Appendix , with no accompanying pink shading of the area below it or calculation of the massive population reduction it implies. Based on a visual comparison of the TFR series on their Fig A5 with comparators I examined in my prior work ). The dashed line, beginning with the upper red rectangle in 1970, shows the counterfactual TFRs based on the aforementioned 16-country comparator [3] through 2020. The gap between the dashed line and the solid line represents the impact of China’s birth planning policies overall.An answer to the first question posited by Gietel-Basten et al.–what would China’s fertility have been like if it had not enacted any birth restrictions at all–requires consideration of a graphic similar to that shown here my . The sollater-longer-fewer. After 1979, the ongoing gap needs to be attributed either to the two-child program, the one-child program, or some combination of each. Yet Gietel-Basten et al. never address the substantial demographic implications of that ongoing gap because in 1979 they switch to a different counterfactual series beginning with the lower red rectangle. In essence, as noted earlier, after 1979 their approach measures only the marginal impact of the tightening to one-child quotas compared to preexisting two-child quotas, not the combined impact of both quotas that results in the ongoing gap.In the 1970s, the gap is clearly due to the two-child program of prior to one-child quotas. After those tightened quotas, despite a saw-tooth pattern in the 1980s which mirrored high-low tides of mass sterilization campaigns [12], there was no appreciable decline in birth rates overall. What is puzzling then is why the strictest one-child phase of the most comprehensive program to control births in history was not followed by a decline in fertility rates. For over 20 years, the typical conclusion by demographers has been the same one repeated by Gietel-Basten et al. [6]: that the one-child program was both ineffective and unnecessary, in the sense that it did not lead to any further decline in birth rates compared to what had already occurred under the two-child quotas of the 1970s and would have otherwise been expected based on developmental factors.Based on their conclusion that the one-child policy shock had a statistically insignificant impact, the ongoing gap after 1979 would presumably have to be fully attributed to the two-child program. Indeed, 14]. Parents living in areas with fines that were double those of other areas tended to have 0.3–0.4 fewer births [16]. Moreover, given that birth rates were falling everywhere else in Asia during the 1980s, it seems likely that they would have fallen in China as well, yet they did not. Counterintuitively, pro-natal policy in the 1980s that propped birth rates up rather than the most anti-natal policy ever enacted.But this standard explanation seems implausible given all we know about what was happening in China at the time. In the early 1980s, nearly one in four Chinese women was sterilized (one out of every 16 women in the world), abortion rates doubled, and strong opposition to one-child limits in rural areas, due in part to son preference, led China in the mid 1980s to allow a “1.5”-child exemption for rural parents whose first child was a daughter [17]. The historical timeline of birth rates in China shows the end result of everything that happened in China’s past, including its policy choices. It does not show us what would have happened in a parallel universe if China had not made the choices that it did at each step along the way.The explanation for this counterintuitive finding is likely related to a fallacy resulting from historical determinism , which is directly related to China’s unique context and sequence of birth policy implementation, I briefly summarize the arguments below.There are several reasons to consider (as outlined below) that if China had 9]. When China began to dismantle the agricultural collectives in the late 1970s, it undermined those very same mechanisms of enforcement [12]. Third, the end of collectivization meant that parents could no longer rely on support from the government to take care of them in their old age. They would now fall back, at least in the short run, on a more traditional source of social security–their own children. Fourth, the one-child program was accompanied, ironically, by a loosening of the later-marriage restrictions that had been enacted in the 1970s. Thereafter, a pent-up demand for childbearing is suggested by the young adults who rushed to get married after 1980, with the average marriage age falling by three years [9]. What resulted was a sharp rise in first births that offset to some extent the sharp decline in second or later births caused by the new one-child program [18].The first reason to expect that birth rates were poised to bounce is that the two-child program of the 1970s had caused fertility to fall so quickly. Thus, many young women during this decade who were prevented from having as many children as they wanted–say, in their 20s –would likely want to make up for that loss a decade later in their 30s. Second, the sharp fall in fertility in the 1970s occurred under enforcements that were effective under collectivized agriculture, where pregnancies were readily monitored (and birth quotas enforced) under communal farming [8]) based on the experience of Taiwan, which had a TFR of 4.0 ten years after falling from the same TFR of 5.8 that China had in 1970. Some observers question whether China’s TFR could have bounced up by more than a full birth after 1979 (from a TFR of 2.8 to 4.0) given that so few countries have ever evinced a rebound of that magnitude [5]. However, China’s unprecedented fertility decline in the 1970s occurred under an unprecedented two-child policy that no other country had ever attempted. Thus, no other country provides a comparative basis for evaluating how high China’s fertility might have bounced back after 1979 had the one-child program not been enacted. Gietel-Basten et al. [6] seem to acknowledge this qualification by asking in their conclusion whether “it is possible to create a ‘synthetic China’ at all?” In general, synthetic comparators for China based on socio-economic indicators may be well-justified in some cases. However, given China’s unique context of birth policy implementation amidst considerable political change, comparative measures from other countries cannot capture the dynamics necessary to measure the impact of one-child restrictions after 1979.In my previous work to isolate the impact of one-child limits, I assumed that China’s TFR would have rebounded to 4 births per woman in the absence of those limits (shown by the green dotted line on 6]– which is observed elsewhere in demographic literature–has yet been put forward. Along with their claim that the one-child program had a statistically insignificant impact on China’s population, their introduction and conclusion both feature a list of its “numerous negative … consequences” which are “impossible to ignore.” These include forced abortions and sterilizations, the distortion of child sex ratios due in part to sex-selective abortion, deliberate under-reporting of over-quota children in surveys, corruption and abuses in the birth planning system itself, as well as “long term psychological consequences of prioritizing one-child families.”[6] In addition to statistical evidence for such negative consequences, personal testimonies of affected parents are also readily available [21]. It seems fair to ask how can such negative consequences resulted from a program otherwise presumed to be statistically insignificant and, thus, apparently inconsequential for childbearing decisions? And if that program was so lacking in consequence for Chinese families, why did so many critics over the last four decades advocate against this program? [21]Not everyone may agree with my interpretation as outlined above. Yet to my knowledge, no other resolution of the central puzzle in Gietel-Basten et al. [