Patients with locally advanced prostate cancer (LAPCa) received docetaxel-based neoadjuvant chemo-hormonal therapy (NCHT) had better clinical outcomes after surgery compared to neoadjuvant hormonal therapy (NHT) groups, but not all patients experienced favorable clinical outcomes with NCHT, raising the necessity for potential biomarker assessment. The transcriptomic profiling offers a unique opportunity to interrogate the accurate response to NCHT and NHT treatment and to identify the predictive biomarkers for neoadjuvant therapy.The whole transcriptomic profiling was performed on baseline biopsies and surgical tissue specimens from 64 patients with LAPCa at Renji Hospital between 2014 and 2018. Biochemical progression-free survival (bPFS)-based gene-by-treatment interaction effects were used to identify predictive biomarkers for guiding treatment selection.Comparing the transcriptome profiling of pre- and post-treatment LAPCa specimens, NHT and NCHT shared 1917 up- and 670 down-regulated DEGs at least 2-fold. Pathway enrichment analysis showed up-regulated pathways in response to NHT and NCHT were both enriched in cytokine receptor interaction pathways, and down-regulated pathways in response to NCHT were enriched in cell cycle pathways. By comprehensive transcriptome profiling of 64 baseline specimens, ten predictive markers were identified. We integrated them into the signature to evaluate the relative benefits of neoadjuvant therapy, which categorizes patients into two subgroups with relative bPFS benefits from either NHCT or NHT. In the high-score (≥ -95.798) group (n = 37), NCHT treatment led to significantly longer bPFS (P< 0.0001), with a clear and early separation of the Kaplan–Meier curves. In the low-score (< -95.798) group (n = 27), NHT also led to significantly longer bPFS (P=0.0025).In this study, we proposed the first predictive transcriptomic signature might potentially guide the effective selection of neoadjuvant therapy in LAPCa and might provide precise guidance toward future personalized adjuvant therapy.The study was approved by the Ethics Committee of Renji Hospital affiliated to Shanghai Jiao Tong University (Ky2019-087). The transcriptomic profiling offers a unique opportunity to interrogate the accurate response to NCHT and NHT treatment and to identify the predictive biomarkers for neoadjuvant therapy. Therefore, we proposed the first predictive transcriptomic signature could potentially guide the effective selection of neoadjuvant therapy in patients with locally advanced prostate cancer in the Chinese population and might provide precise guidance toward future personalized adjuvant therapy.Prostate cancer (PCa) remains one of the most commonly diagnosed malignancies worldwide . NowadayNeoadjuvant hormonal therapy (NHT) before RP for LAPCa has shown promises in local disease control, but not in survival outcomes in randomized clinical trials . NeoadjuWith the progression of high throughput gene-sequencing technology, understanding of tumor genome and the development of tumor molecular typing has been greatly promoted. RNA sequencing (RNA-seq) contributes to the accurate classification of PCa to guide the selection of treatment options and helps to explore the molecular mechanism of drug resistance in PCa , 12. HowIn this study, we conducted a comparative study on patients with LAPCa who received NCHT or NHT before RP. We compared the transcriptomic landscape of NHT and NCHT to investigate the gene expression changes in patients with LAPCa in response to docetaxel in the neoadjuvant treatment. Then we further screened the predictive markers from baseline RNA-seq data and built the predictive signature to guide the optimum selection of the neoadjuvant treatment.From 2014 to 2018, a total of 338 patients whom had LAPCa intended to receive neoadjuvant therapy and RP in Renji Hospital. Patients were enrolled according to the following criteria  and docetaxel-based systemic chemotherapy were administrated. Docetaxel was administrated intravenously at a dose of 75 mg/mThe RNA samples were isolated and sent to the Mingma technologies  for constructing the RNA-seq library. Briefly, total RNA was extracted by the RNeasy Mini Kit . RNA quantity and quality, with the calculation of RNA integrity number (RIN), were accessed by The NanoDrop 2000 spectrophotometer (Thermo Scientific) and 2100 Bioanalyzer (Agilent). Poor samples were excluded by RIN< 8 or total RNA< 1.1 ug. Samples were selected for strand-specific total RNA library preparation in a TruSeq Stranded Total RNA Gold library protocol , which is recommended for low input amounts or degraded RNA samples . After nWe followed the popular approach to select predictive genes and develop the predictive signature –17. We f0 (t)h is the baseline hazard and β are the regression parameters, gi is the gene expression level, it denotes the treatment assignment for each patient i, such that it = 0 indicates that the patient received NCHT and it = 1 indicates that the patient received NHT, and the product igit represents the interaction term of treatment and the gene expression level.Where β3 = 0. A standardized test statistic, z, that approximately follows the standard normal distribution under the null interaction effect was calculated by:The selection of biomarker is based on the Wald test statistic for testing a null interaction effect, β3=0). After standardization, a negative or positive z value represents that high gene expression is associated with better outcomes with NHT or NCHT, respectively. A set of features with a significance level of this test statistic less than 0.05 were selected to generate the multi-gene signature.Here, z is the standard Wald statistic of the multi-factor Cox proportional hazards model without interaction  of the ten predictive markers, we could calculate the score using the following function:Smaller values of score correspond to a greater chance of benefiting from NHT than from NCHT. Based on the combination of predictive genes of each score, we screened the different cutoffs of the predictive signature and chose to categorize the patients into two subgroups with scores of<−95.798 and ≥−95.798 with distinct biochemical progression-free survival (bPFS) differences.Genes with DEseq2 |logFC|>1 and adjust-pval<0.05 were identified as differentially expressed genes (DEGs). For The significantly enriched pathways and the detailed results were presented in Here, A and B represent the two gene lists, and |A∩B| and |A∪B| represent the intersection and union of the two gene lists, respectively. The Jaccard similarity coefficient ranges from 0 to 1, with a value closer to 1 indicating a higher similarity between the two gene lists. The results of the calculation can be found in The original enrichment results of down-regulated pathways after the two treatments can be found in Univariate and multivariate analyses of the association of biomarkers, treatment interaction, and clinical factors with bPFS were performed with the Cox proportional hazard regression model. Means and differences of the means with a 95% confidence interval (CI) were calculated using Wilson’s score CI. A p-value of<0.05 was considered statistically significant. Differences between the groups were calculated using the Wilcox test, or the Fisher’s exact test, where appropriate. Kaplan–Meier curves of bPFS were estimated for each subgroup, and statistically compared using the log-rank test. A log rank test P-value< 0.5 was considered statistically significant. All statistical analyses were performed with the R statistics package .Total of 64 LAPCa patients with available baseline biopsy specimens have been enrolled for transcriptomic profiling Figure 1The bPFS survival profiles of patients in NCHT and NHT groups were shown in ERG)  and CLYBL  were the strongest predictors for neoadjuvant therapies but in opposite directions. Here, the high expression of GNAS indicated relative benefit with NCHT, while the high expression of CLYBL indicated relative benefit with NHT.In this study, we adopted the popular approach of testing bPFS-based gene-by-treatment interaction effects to identify predictive biomarkers for guiding treatment selection –19. We eWe then constructed a predictive signature to quantitatively assess individual tumors and their corresponding treatment responses by weighting z scores from individual treatment-by-interaction tests of those selected genes. Further, to stratify patients for particular treatment benefits, we screened the different cutoff values of the predictive signature and chose to categorize the patients into two subgroups at -95.79 as they resulted in the best bPFS differences . The patients/participants provided their written informed consent to participate in this study.BD and WX designed research; YZ, LF, HZ and YG performed research; CC, YW, and JP contributed new reagents/analytic tools; YZ and LF wrote the paper. All authors contributed to the article and approved the submitted version.


FGF14 are a common cause of spinocerebellar ataxia . Molecular confirmation of FGF14 GAA repeat expansions has thus far mostly relied on long-read sequencing, a technology that is not yet widely available in clinical laboratories. We developed and validated a strategy to detect FGF14 GAA repeat expansions using long-range PCR, bidirectional repeat-primed PCRs, and Sanger sequencing. We compared this strategy to targeted nanopore sequencing in a cohort of 22 French Canadian patients and next validated it in a cohort of 53 French index patients with unsolved ataxia. Method comparison showed that capillary electrophoresis of long-range PCR amplification products significantly underestimated expansion sizes compared to nanopore sequencing  and gel electrophoresis . The latter techniques yielded similar size estimates. Following calibration with internal controls, expansion size estimates were similar between capillary electrophoresis and nanopore sequencing , and gel electrophoresis . Diagnosis was accurately confirmed for all 22 French Canadian patients using this strategy. We also identified 9 French patients  and 2 of their relatives who carried an FGF14 (GAA)≥250 expansion. This novel strategy reliably detected and sized FGF14 GAA expansions, and compared favorably to long-read sequencing.Dominantly inherited GAA repeat expansions in  Despite recent advances in our understanding of the genetic basis of these conditions, a genetic diagnosis is reached in less than 50% of patients with LOCA4. Dominantly inherited GAA repeat expansions in the first intron of the fibroblast growth factor 14 gene (FGF14) have recently been reported as a common cause of LOCA , accounting for 10 to 61% of unsolved cases in various cohorts6. Current data support a pathogenic threshold of (GAA)≥250 repeat units. Core clinical features of GAA-FGF14 ataxia include slowly progressive cerebellar ataxia, early episodic symptoms, downbeat nystagmus, diplopia, and dizziness/vertigo5. Molecular confirmation of the FGF14 GAA repeat expansion has thus far mostly relied on long-read sequencing, a technology that is not yet widely available in clinical diagnostic laboratories. Given the high reported frequency of GAA-FGF14 ataxia, there is an immediate need to establish a standardized, accessible, and validated molecular strategy for diagnosing this novel repeat expansion disorder in clinical diagnostic laboratories.Late-onset cerebellar ataxias (LOCAs) are a group of neurodegenerative conditions that have until recently largely challenged molecular diagnosisFGF14 GAA repeat expansions in clinical diagnostic settings. We further demonstrate the applicability of this diagnostic approach in a cohort of French patients with unsolved LOCA.Herein, we propose a strategy that combines long-range polymerase chain reaction (LR-PCR), bidirectional repeat-primed PCRs (RP-PCRs), and Sanger sequencing to detect and resolve All participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki.FGF14 ataxia and six controls that were reported previously5. All participants underwent genotyping of the FGF14 repeat locus by agarose gel electrophoresis of LR-PCR amplification products and targeted long-read nanopore sequencing, as described previously5.Patients were recruited at the Montreal Neurological Hospital of the McGill University Health Centre . The institutional review board of the McGill University Health Centre approved this study (MPE-CUSM-15-915). The French Canadian participants included 22 patients with GAA-7 was recorded when possible. Magnetic resonance imaging (MRI) of the brain was obtained for nine patients with GAA-FGF14 ataxia.Fifty-three index patients with LOCA and two affected relatives were recruited at the Centre Hospitalier Régional Universitaire de Nancy (France). The institutional review board of the Centre Hospitalier Régional Universitaire de Nancy approved this study (2020PI220). Patients were enrolled if meeting the following inclusion criteria: (a) progressive ataxia with onset at or after age 30; (b) no clinical features suggestive of multiple system atrophy (MSA); (c) exclusion of acquired causes; and (d) exclusion of known genetic causes by testing negative on an ataxia gene panel and for common coding and non-coding repeat expansion disorders. The Scale for the Assessment and Rating of Ataxia (SARA)The primer sequences and experimental conditions are provided in Supplementary Table FGF14 GAA repeat locus was amplified by fluorescent long-range PCR (fLR-PCR). fLR-PCR products were analyzed on an ABI 3130xl, 3500xl, or 3730xl DNA Analyzer  with a 50-cm POP-7 capillary using the GeneScan 1200 Liz Dye Size Standard  following the manufacturer’s instructions  and the 3′ end (3′ RP-PCR) of the locus were used in parallel to ascertain the presence of a GAA expansion at the repeat locus. RP-PCR products were analyzed on an ABI 3130Samples with a single normal allele detected on fLR-PCR2.Samples with at least one allele of 250 or more repeat units detected on fLR-PCR and no sawtooth profile or interrupted profile on RP-PCRs.Cases with a single normal allele detected by capillary electrophoresis of fLR-PCR products, regardless of their RP-PCR profiles, next underwent gel electrophoresis of LR-PCR amplification products using a 1.5% agarose gel. This step allows for distinguishing cases homozygous for two normal alleles from cases heterozygous for one normal allele and one expanded allele that is too large  to be detected by capillary electrophoresis. The motif of any expansion identified at this stage is determined by the RP-PCR profiles generated during step 2, with sawtooth profiles indicating GAA expansions and flat profiles indicating non-GAA expansions. Amplification products were migrated for 3h30 at 150 V and 300 mA. Agarose gels were imaged with the Uvidoc HD6 Gel Documentation System (Uvitec) using the default UV-Gel settings in the Uvitec-1D software. The molecular weight of PCR amplification products was measured by the molecular weight detection tool in the Uvitec-1D software (Uvitec).FGF14 locus. Sanger sequencing of LR-PCR amplification products using the BigDye Terminator v1.1 Sequencing kit  was performed using the ABI 3130xl DNA Analyzer . We further assessed the effect of DNA shearing on the results of the proposed strategy (see Supplementary Methods and Supplementary Results).mcreg). Confidence interval of the slope that does not include 1 indicates statistically significant evidence of a proportional bias between two methods. Confidence interval of the intercept that does not include 0 indicates statistically significant evidence of a systematic bias between two methods. Linear relationship between two sets of measurements was tested by the CUSUM test of linearity. We used the Bland–Altman analysis to compare the measurements of the same variable by two methods (GraphPad Prism 9). Correlations were calculated using the Pearson correlation coefficient. We analyzed the data in R (version 4.1) and GraphPad Prism 9. P value of < 0.05 was considered significant. All analyses were two-tailed.We compared methods against each other using the Passing-Bablok regression model  targeted long-read nanopore sequencing and (2) LR-PCR estimates by agarose gel electrophoresis. We used 28 French Canadian participants for whom long-read sequencing data was available and measured repeat length by fLR-PCR. Nanopore sizing was performed on size-selected alleles of more than 80 repeat units to increase coverage depth, as described previously>400 repeat units could not be accurately sized by fLR-PCR, as shown in Fig. We observed that expansions (GAA)12, we observed a significant discrepancy between sizes of larger alleles measured by capillary electrophoresis and long-read sequencing or agarose gel electrophoresis. We compared methods using the Passing–Bablok regression model13, and found a statistically significant proportional bias between nanopore sequencing and fLR-PCR  and gel electrophoresis and fLR-PCR   of the French Canadian cohort carrying an FGF14 expansion. To compensate for this underestimation, we used a set of four alleles whose sizes have been established by long-read sequencing and applied the least squares method to generate a straight line that fits our data and calculated its slope and y-intercept 250–300 alleles appear incompletely penetrant6. To address this need, we developed and validated a three-step strategy combining (1) fLR-PCR, (2) bidirectional RP-PCRs, and (3) agarose gel electrophoresis of LR-PCR amplification products and Sanger sequencing.The variability of the methods used thus far to resolve FGF14 repeat locus. The identification of two alleles of less than 250 repeat units ends the diagnostic process while any other pattern triggers additional testing. That includes samples with a single normal allele detected on fLR-PCR, since they may carry an expansion larger than 400 repeat units that falls beyond the limit of detection of capillary electrophoresis.The first step of our proposed strategy involves fLR-PCR amplification of the FGF14 ataxia and ends the diagnostic process. All other samples are moved to the third step.The second step involves bidirectional RP-PCRs targeting the GAA repeat unit at the 5′ end and the 3′ end of the locus. Bidirectional RP-PCRs allow for a comprehensive assessment of the repeat motif over the entire length of expansions and of sequence interruptions and polymorphisms at both ends of the locus. The identification of characteristic saw-toothed products, indicative of a GAA repeat expansion, in a sample found to have at least one allele of 250 or more repeat units on fLR-PCR confirms the diagnosis of GAA-FGF14 locus. Although Sanger sequencing of each strand fails to completely sequence the expanded alleles, thus making this technique unreliable for sizing expanded alleles, their combination allows for sequence coverage of alleles of up to about 500 triplet repeat units.As part of the third step, gel electrophoresis of LR-PCR products and Sanger sequencing are used to further resolve the remaining samples. First, gel electrophoresis of LR-PCR products is performed on a 1.5% agarose gel for samples with a single allele detected by fLR-PCR, regardless of their RP-PCR profiles. This step allows for distinguishing cases homozygous for two normal alleles from cases heterozygous for one normal allele and one expanded allele that is otherwise too large to be detected by capillary electrophoresis. Gel electrophoresis additionally allows the sizing of large expansions. The motif of any expansion is determined by the RP-PCR profiles generated during step 2, with sawtooth profiles indicating GAA expansions and flat profiles indicating non-GAA expansions. In the case of non-GAA expansions, Sanger sequencing can be performed to determine the repeat motif of the expansion. Second, Sanger sequencing is performed on samples with at least one allele of 250 or more repeat units measured by capillary electrophoresis and a flat or atypical profile on RP-PCRs—indicative of a non-GAA or interrupted expansion—to determine the repeat motif of the ≥250 repeat expansions in FGF14. The expansion was also present in two affected relatives of one of the index patients  who carried (GAA)Our diagnostic approach also allowed for the precise characterization of the sequence of each allele in the compound heterozygous patient Fig. C–E. WhilFGF14 ataxia are shown in Table 2 = 0.21; p = 0.031) or the age at onset of permanent ataxia  in the French Canadian cohort. In comparison, the size of the repeat expansion was not significantly associated with age at disease onset  or the age at onset of permanent ataxia  in the French cohort.The main clinical features of the French Canadian and French patients with GAA-467 allele resulted in expansion in the female germline in two meiotic events (expansion from 467 triplets to 492 and 510 triplets) ≥250 uninterrupted repeat units6. Pending additional data on the effect of interruptions on the pathogenicity of FGF14 expansions, we suggest that alleles with a minimum repeat tract of 250 uninterrupted triplets be considered pathogenic.We also recommend using bidirectional RP-PCRs targeting both ends of the repeat locus during the second step to interrogate the sequence motif over the entire length of larger expansions, which cannot otherwise be achieved with a single RP-PCR. This further allows for a comprehensive assessment of any potential sequence interruptions and polymorphisms at both ends of the locus. While sequence interruptions are relatively common in Friedreich ataxia, an autosomal recessive disorder caused by a GAA repeat expansion in the ≥250 expansion as well as two of their affected relatives. The frequency of the FGF14 GAA expansion in our cohort is similar to that of other reported cohorts of European descent6. Patients from the French Canadian and French cohorts had a similar phenotype that included frequent nystagmus and episodic symptoms at onset followed by a slowly evolving pan-cerebellar syndrome at an average age of 59 years in the French Canadian cohort and 66 years in the French cohort. A substantial proportion of patients also reported marked alcohol intolerance that could precede the development of ataxia by a number of years in some patients. GAA-FGF14 ataxia is phenotypically similar to SCA615 and the first late-onset episodic ataxia described thus far. Other genetically defined episodic ataxias, such as episodic ataxia type 1 or 2, often manifest in childhood18. The observation of a cerebellar syndrome of late-onset associated with episodic symptoms, downbeat nystagmus, and alcohol intolerance should prompt testing for the FGF14 GAA expansion. As with other repeat expansion disorders19, we found an inverse correlation between the size of the expansion and the age at onset in the French Canadian cohort. However, we did not observe such a correlation in the French cohort, likely as a result of the small cohort size.Applying this strategy to a cohort of 53 French patients with unsolved LOCA, we identified nine patients (17%) who carried at least one (GAA)467 expansion. These results further highlight the instability of the FGF14 repeat locus upon meiotic transmission.We also found expansion in the female germline across two meiotic events in an affected mother carrying a (GAA)FGF14 expansions may result in allele dropout and false negative results on LR-PCR and RP-PCRs. Sequence polymorphisms at the primer binding sites may similarly result in allele dropout on LR-PCR and RP-PCRs. While we have shown that this strategy can be successfully implemented in clinical diagnostic laboratories, the number of different molecular techniques involved, some of which are time-consuming, may place an additional burden on the technical team of diagnostic laboratories. This is especially relevant given the large shear of patients that are likely to undergo testing for GAA-FGF14 ataxia.The results of this study must be interpreted in light of some limitations. While we tested and validated this diagnostic strategy in two independent patient cohorts, the sample size of both cohorts was limited. The study of larger cohorts in which the performance of this strategy will be orthogonally confirmed by long-read sequencing will be necessary to validate the results presented herein. Further validation of this strategy in other independent diagnostic laboratories will be required as it remains to be established whether the use of other genetic analyzers or capillary electrophoresis instruments may influence the performance of this strategy. We cannot exclude that the modification of any of the analytical parameters used in this study will not influence the performance or results of the molecular strategy. Furthermore, our results cannot exclude the possibility that degradation of DNA from patients carrying FGF14 ataxia as a common cause of LOCA highlighted the need to develop a reliable diagnostic test for this novel condition. To address this need, we developed and validated an approach to diagnosing GAA-FGF14 ataxia that is reproducible, easy to implement in clinical settings, and does not rely on long-read sequencing. We implemented this protocol in a clinical laboratory and showed its successful application in a cohort of patients with unsolved LOCA.In conclusion, the recent description of GAA-Supplementary Information 1.Supplementary Information 2.


Food quality assurance is an important field that directly affects public health. The organoleptic aroma of food is of crucial significance to evaluate and confirm food quality and origin. The volatile organic compound (VOC) emissions (detectable aroma) from foods are unique and provide a basis to predict and evaluate food quality. Soybean and corn oils were added to sesame oil (to simulate adulteration) at four different mixture percentages (25–100%) and then chemically analyzed using an experimental 9-sensor metal oxide semiconducting (MOS) electronic nose (e-nose) and gas chromatography–mass spectroscopy (GC-MS) for comparisons in detecting unadulterated sesame oil controls. GC-MS analysis revealed eleven major VOC components identified within 82–91% of oil samples. Principle component analysis (PCA) and linear detection analysis (LDA) were employed to visualize different levels of adulteration detected by the e-nose. Artificial neural networks (ANNs) and support vector machines (SVMs) were also used for statistical modeling. The sensitivity and specificity obtained for SVM were 0.987 and 0.977, respectively, while these values for the ANN method were 0.949 and 0.953, respectively. E-nose-based technology is a quick and effective method for the detection of sesame oil adulteration due to its simplicity (ease of application), rapid analysis, and accuracy. GC-MS data provided corroborative chemical evidence to show differences in volatile emissions from virgin and adulterated sesame oil samples and the precise VOCs explaining differences in e-nose signature patterns derived from each sample type. Adulteration and fraud are important issues that have affected the commercial production and marketing of plant-based oils , and they have gained significant attention in the sesame oil industry in recent years ,2. SesamFood quality reports worldwide have had a major focus on issues relating to the quality and adulteration of edible oils, accounting for 24% (the highest contribution) among the important issues discussed within such reports . ValuablThe detection of sesame oil fraud and adulteration involves the identification and quantification of adulterants, contaminants, or additives that compromise the quality and authenticity of the product. Various analytical techniques and methods have been developed to address this challenge. These methods aim to accurately determine the composition and origin of sesame oil, distinguishing it from adulterated or counterfeit products . One comUsing analytical techniques with chemical sensors for the detection and determination of VOCs is an excellent approach to evaluate the quality of oil samples and to distinguish original high-quality vegetable oils from adulterated, corrupted, or externally spoiled oils . The conThe objectives of this research were to (1) evaluate the capabilities of electronic nose technology as a rapid, low-cost, practical tool for the detection and discrimination of pure sesame oil from adulterated versions containing soybean and corn oils at different mixture percentages, and (2) to compare e-nose output data with GC-MS analytical data showing chemical differences in vegetable oil sample types. This study aimed to investigate the physical and chemical characteristics of pure sesame oil compared to adulterated mixtures for human health considerations and to preserve the high-cost value of sesame oil by improving quality assurance methods. By employing the electronic nose, the research seeks to enhance capabilities to accurately detect differences between genuine sesame oil and mixed or adulterated oils, ultimately contributing to the prevention of adulteration fraud in the sesame oil industry.Sesamum indicum), also called benne, of cultivar ‘Yekta’ were used for oil extraction. Yekta is a new sesame cultivar derived from a genetic cross (B5 × M7). The ‘Yekta’ cultivar matures early and is recommended for northern parts of Iran at altitudes >1000 m above sea level. Iranian sesame is considered a good source of natural antioxidants for medicinal and commercial uses. After preparing the sesame seeds, oil was rendered from the seeds using a mechanical pressure device, a BD 65 Oil Cold Press Machine , followed by several steps of seed oil filtration, performed to remove solid impurities and contaminants. Samples of pure, genuine sesame oil and mixed adulterated sesame oil (containing different percentages of soybean and corn oil additives) were evaluated in this research. Four adulteration levels  were analyzed and evaluated for each of the two types of added adulterant oil, resulting in a total of 9 separate treatments (oil sample types) with 15 replications of each sample type prepared for each treatment. Fifteen separate glass vials containing 100 mL of each oil sample type, consisting of pure sesame oil and the four adulterated versions, were prepared as replications for e-nose analysis. Nine additional vials containing 100 mL of each oil sample type were prepared and used for GC-MS testing.Iranian sesame seeds  is the normalized signal, Xs(t) denotes the output signal of the sensor due the presence of volatile substances (VOC), and Xs(0) shows the base signal of the sensors or baseline [Data preprocessing was required before extraction of data features to reveal the relative responses of sensors and to increase their accuracy in the pattern detection analysis . The datbaseline .Chemometrics is a branch of chemistry which utilizes mathematics, statistics, and logic to obtain further information on the chemical systems, design, and selection of optimal empirical processes and provide optimal chemical data . ChemomePCA is an unsupervised multi-variable statistical analysis method for linear data compression, decreasing the data dimension, and feature extraction. PCA provides the possibility to detect outliers, pattern recognition in the sample distribution, and inter-variable relationships and classes . This meANN is a method classified in the artificial intelligence class of computer science with fundamental differences from other computational methods. Multi-layer perceptron neural network is a feed-forward network, which can be utilized in chemometrics analysis to solve pattern recognition and prediction problems using either supervised or unsupervised methods ,36.A gas chromatograph used in tandem with a mass spectrometer (GC-MS) with a 30 m long HP-5 column, having an internal diameter of 0.250 mm, was utilized to identify the VOC chemical parameters of the vegetable oils. This device was set up for injecting liquid samples with split/splitless inlet dilution. Mass spectrometer detector (MSD) analysis was employed for both qualitative and quantitative determinations. The detector was equipped with an EI-type ionization system with a single quadrupole MS analyzer. An EMP triple-axis detector with very low noise and drift was employed to achieve high sensitivity.w/v) NaOH, prepared in dry methanol. The test tube was kept in a water bath for 10 min. A 3 mL aliquot of BF3 reagent was added to the mixture kept in the warm-water bath at 60 °C for an additional 10 min. After the reaction, the test tube was placed in a cold-water bath at 1 °C, and 2 mL of 20% NaCl with 1 mL of hexane were added and stirred to a uniform mixture. The resulting mixture was centrifuged at 4000 rpm for 10 min, and the hexane layer containing methyl ester of fatty acids was separated for subsequent GC-MS analysis [All oil samples were prepared as methyl ester derivatives of fatty acids using the Savage and McNeil 1998) method , slightl998 methoFive evaluation criteria  were used to assess the performance of the statistical models. AUC is the area under the ROC curve that represents the tradeoff between Recall and Specificity. Four parameters of true positive (TP), false positive (FP), true negative (TN), and false negative (FN) were used to calculate the evaluation criteria. Accuracy was determined based on the ratio of correctly classified samples (TP and TN samples) to the total number of samples. Sensitivity was indicated by the ratio of TP samples to the total number of positive samples (TP and FN samples). Precision was determined by the ratio of TP samples to the total number of positive predictions (TP and FP samples). F1 score was determined as the harmonic mean of precision and sensitivity .The GC-MS analyses of sesame oil (with and without adulterations with lower-cost oils) were compared to published information on the major fatty acid composition of these individual vegetable oils as follows. Sesame oil primarily contains linoleic acid  and oleic acid , accounting for 80% of the total fatty acids present . Corn oiGC-MS analysis revealed eleven major compounds representing about 82–91% of the oil, as listed in Cluster analysis was conducted using GC-MS fatty acid data to classify and detect adulteration in sesame oil. The results were plotted in a heat map . The heaEuclidean distance (ED) measures showed the level of chemical relatedness of VOC emission components (quantitative VOC content) from each oil sample type , providing indications of chemical differences between oil samples based on specific volatile fatty oils detected using GC-MS analysis. Notice that pure sesame oil contained very little stearic acid (major fatty acid component) and also low levels of the other eight minor fatty acid components (gondoic acid–erucic acid) at the bottom of the heat map  compared to all other pure oil types and sesame mixtures containing added soybean or corn adulterants that did contain the stearic acid as a major VOC component . However, pure sesame oil did share the presence of high levels of palmitic acid with all other pure oil types and adulterated sesame oil mixture types . Pure sesame oil was the only oil sample type that contained high levels of linoleic acid .The unique aroma signatures, based on the maximum sensor outputs recorded during an e-nose analytical run  derived from the nine-sensor MOS gas sensor array, are presented in Radar plots, displaying individual sensor output responses (composing the smellprint patterns) from the e-nose sensor array, provided indications of relative differences in sensor intensities to different vegetable oil VOC components . Mean sePCA indicated differences between oil sample types based on the recognition of pattern differences in e-nose sensor output data responses, in which the higher the PCA total variance, the greater the discrimination between sample types . The valThe LDA method was utilized to reduce the classification differences and enhance discrimination between different oil groups based on e-nose sensor responses. The variance in the total samples for the LDA method was 94.81 according to the LDA results presented in Pattern recognition methods are a valuable alternative for data cluster separation, but this task becomes more difficult in systems with large amounts of data and overlapping data plot regions. Therefore, machine learning algorithms are more effective for independent sample classification to optimize and automate data processing, reduce subjectivity, and increase efficiency in predicting results and method robustness ,21. SVM SVM was used as a supervised pattern recognition method to detect adulteration in pure sesame (S) oil from SB- and C-type oil-adulterated samples. Two types of statistical models, based on SVM methods (C-SVM and Nu-SVM), were used. Nu, C, and γ parameters were validated by trial and error and through error minimization. Four types of linear, polynomial, radial base, and sigmoid kernels were applied to classify pure S-type oil from SB- and C-type oil mixtures. The performance of SVM models was evaluated based on classification accuracy . The hig2 values of 0.956 and 0.936 for training and test analyses. The RMSE values were 0.001 and 0.018 for training and validation tests, respectively. The model had a total recognition accuracy of 95.6%. Regarding the lower performance value for the training phase (compared to the testing phase), there were no signs of under- or overfitting.ANN results are also depicted in The disturbance matrix and functional parameters of the ANN and C-SVM models are compared in E-nose fatty acid VOC analysis data, derived from sensor responses to oil sample headspace volatiles, combined with statistical methods for data analysis, yielded an effective means of distinguishing between pure sesame oil and two other different vegetable oil types (soybean and corn) in various adulterated mixtures with sesame oils in the current study. A comparison of e-nose data analysis results, using four separate statistical models to determine their effectiveness in discriminating between pure sesame oil and other adulterated mixtures of this oil, was based on e-nose analysis of VOC emissions that consisted primarily of volatile fatty acids present in the headspace of each oil sample type. GC-MS data provided the analytical chemical support data to show the basis for chemical differences in VOC emissions (from different oil sample types), which backed up and confirmed related differences revealed from e-nose analysis of the different sample types revealed from unique e-nose smellprint signatures recorded for pure vs. adulterated sesame oil samples from the sensor array.Combining e-nose and statistical models for comparisons of volatile compound emissions from oil samples provided a qualitative means for assessing oil quality through the detection of adulteration, oil product spoilage, or deterioration by oxidation and other external factors, necessary for confirming the authentication of oil purity and quality to avoid product fraud. GC-MS data indicated that it was often difficult to detect the adulteration of sesame oil with both soybean and corn oil because the fatty acid composition of these oils is very similar with high amounts of palmitic acid (major VOC components), but pure sesame oil is very low in stearic acid content. However, the presence of much higher levels of linoleic in pure sesame oil than in the other, lower-quality soybean and corn oil types provided another major discovered difference in VOC emissions. These two major VOC emission differences between pure sesame oil and other oil types and mixtures made it possible to effectively detect these VOC differences in e-nose sensor-array response patterns, indicative of adulteration obtained through statistical analysis of sensory data derived from the MOS electronic nose. The combination approach of using an MOS e-nose with appropriate statistical modeling provided results indicating that this is a promising approach for detecting fraudulent adulterations of sesame oil. PCA explained 96% of the total variance  in e-nose data when comparing pure sesame oil with other oils and sesame oil mixtures. However, the SVM linear model method, along with the Nu-SVM method, showed the highest classification accuracies. Based on LDA and QDA statistical methods, the oil classification accuracy was 94.07 and 100%, respectively. The best discrimination occurred while using 9-10-9 topology that yielded a 95.6% correct classification rate for this neural network method.Our results for sesame oil e-nose analyses and GC-MS are compared here with published results in the following discussions, which describe other study results that have used GC-MS and/or e-nose devices to analyze different types of vegetable oils for various product quality and purity-certification applications. These studies showed how the methods used in oil production are important in determining final product quality.Zunin et al.  used a qBougrini et al.  used an Lerma-García et al.  also useCerrato Oliveros et al.  employedA relatively recent review article by Roy and Yadav  comparedMen et al.  analyzedw/w) of 1–10% in increments of 1% and in a range of 10–20% adulteration with 5% increments. Headspace volatiles from oil samples were analyzed, and the best separation of adulterated samples was achieved using PCA, especially when the level of adulteration increased, contributing to 91% accuracy in the data. The PLS study results yielded a coefficient of determination (R2) value of 0.91, indicating the potential utilization of the e-nose in the adulteration detection of virgin coconut oil with palm kernel olein oil.Marina et al.  investigHong et al.  analyzedThe adulteration of peony seed oil with corn oil, rapeseed oil, sunflower oil, and soybean oil was investigated in a study by Wei et al.  using thThe electronic nose and GC-MS chemical analysis methods (used in combination) were evaluated in this study for the capability of detecting the adulteration of sesame oil due to amendments with soybean and corn oils, added and mixed at different ratios and concentrations. We propose that the e-nose-based method alone is a simple and low-cost approach to quickly detect adulteration in sesame oil. This method may be utilized as a complementary technique to the quality control and assurance of sesame oil purity. Additional investigations are needed to further optimize experimental parameters and data collection to improve this technique and to fully validate the proposed method for differentiating between pure sesame oil from adulterated mixtures with lower-quality oils at different concentrations. Once e-nose analysis discriminations are confirmed using GC-MS chemical analysis data, e-nose analysis can be accomplished alone without supporting chemical analysis data. The advantages of using e-nose analysis are that it is much faster, cheaper, easier, and more effective in the discrimination and identification of vegetable oil sample types, compared to GC-MS analysis that requires considerably higher expense, more laborious interpretation of chemical data, and much longer delays in obtaining analysis results.Different methods used with various e-nose instruments described in the literature for various applications related to oil quality control, including detecting adulteration, determining regional origin, and evaluating the impact of production factors on the final product, have demonstrated the versatility of e-nose technologies for oil quality assessments. The choice of the specific e-nose method used depends on the desired objectives and the characteristics of the oils being analyzed. The emergence of e-nose instruments as valuable tools for oil quality analysis has revolutionized the detection of adulteration in vegetable oils. By employing electronic chemical sensors and pattern recognition systems, e-noses have the capability to analyze VOCs emitted by various oil samples. This technology presents a rapid and non-destructive approach for evaluating the authenticity and quality of oils, including the identification of adulteration. The application of an e-nose in the oil industry has demonstrated promising outcomes in distinguishing between unadulterated and adulterated oils. E-nose instruments can effectively detect subtle discrepancies in VOC profiles, enabling the identification of unauthorized substances added to quality oils or the presence of lower-quality oils mixed with higher-quality ones. Through the utilization of statistical models and machine learning algorithms, the e-nose can offer the precise and reliable classification of oil samples, guaranteeing consumer protection and preventing fraudulent practices. E-nose methods are easier to use (simplicity of operation), provide greater speed in terms of analytical results at lower costs, and have the capabilities to handle intricate odor profiles. They serve as a complementary technique to traditional analytical methods, such as GC-MS, by facilitating real-time and on-site monitoring, thereby reducing the reliance on expensive and time-consuming laboratory testing. Moreover, the e-nose contributes to the assessment of oil freshness, stability, and oxidation levels, all of which are important factors in determining overall oil quality. The implementation of e-nose technologies provides an effective solution to inadequate conventional methods for the detection of oil adulteration and the assurance of product integrity. The capacity of e-nose instruments to nondestructively analyze VOC emissions from oils makes them effective and efficient tools for assuring post-harvest quality control in oil products to combat adulteration fraud and safeguard consumer interests.


Child mortality continue to be a major public health issue in most developing countries; albeit there has been a decline in global under-five deaths. The differences in child mortality can best be explained by socioeconomic and environmental inequalities among countries. In this study, we explore the effect of country-level development indicators on under-five mortality rates. Specifically, we examine potential spatio-temporal heterogeneity in the association between major world development indicators on under-five mortality, as well as, visualize the global differential time trend of under-five mortality rates.The data from 195 countries were curated from the World Bank’s World Development Indicators (WDI) spanning from 2000 to 2017 and national estimates for under-five mortality from the UN Inter-agency Group for Child Mortality Estimation (UN IGME).We built parametric and non-parametric Bayesian space-time interaction models to examine the effect of development indicators on under-five mortality rates. We also used employed Bayesian spatio-temporal varying coefficient models to assess the spatial and temporal variations in the effect of development indicators on under-five mortality rates.In both parametric and non-parametric models, the results show indicators of good socioeconomic development were associated with a reduction in under-five mortality rates while poor indicators were associated with an increase in under-five mortality rates. For instance, the parametric model shows that gross domestic product (GDP) , current healthcare expenditure  and access to basic sanitation  were associated with a reduction under-five mortality. An increase in the proportion practising open defecation  an increase under-five mortality rate. The result of the spatial components spatial variation in the effect of the development indicators on under-five mortality rates. The spatial patterns of the effect also change over time for some indicators, such as PM2.5.The findings show that the burden of under-five mortality rates was considerably higher among sub-Saharan African countries and some southern Asian countries. The findings also reveal the trend in reduction in the sub-Saharan African region has been slower than the global trend.The online version contains supplementary material available at 10.1186/s12942-023-00330-x. The global burden of child mortality is disproportionate high among countries with limited resources or the so-called “low- and middle-income countries” (LMIC). For instance, in 2018 the under-five mortality rate (U5MR) in the Sub-Saharan African region was approximately twice (78 per 1000 live births) the global rate of 39 deaths per 1000 live births –8Socioeconomic gradient in health and wellbeing is a ubiquitous in public health. The evidence in existing studies show that persons of low socioeconomic status have higher morbidity and mortality compared to those of higher socioeconomic position , 6, 9. LIncreasingly, the research shows that investments aimed at improving socioeconomic status and reducing environmental risk exposures reduce U5MR. For instances, findings from studies in Malawi, Uganda, India, Nigeria, and other developing countries have found evidence for a positive causal relationship between education and child mortality , 18. TheExamine the spatio-temporal effects of key global development indicators on under-five rates among countries of the world.Determine if there are spatial and temporal variations in the association between the development indicators and under-five rates.Map and visualize these spatial and temporal differences in the association between key global development indicators and U5MR.Nonetheless, there is limited research on the spatio-temporal effect of socioeconomic development and environmental indicators on global U5MR. Existing studies have also failed to explore potential spatial and temporal heterogeneity in the association between these development and environmental indicators and U5MR. In order to meet the Sustainable Development Goals (SDGs), particularly improve health and reduce spatial inequalities in health, there is the need to incorporate spatial-temporal perspectives in the current discourse on global health. In view of the current gap in the research, our study seeks to explore the spatio-temporal effect of global development indicators on U5MR. Specifically, the study sought to:The data from this study was derived from the World Bank’s World Development Indicators (WDI). The WDI data contains statistical information on major global development indicators compiled from officially-recognised international sources, including national statistics bureaus. The WDI data is available at national, regional and global levels. Given the focus of our study, we used the national estimates from 195 countries. The original data had information on 217 countries and territories. In this study we excluded territories of sovereign states; that is, data on territories such as the Faroe Islands and the British Virgin Islands were excluded from the study. A full list of the countries used in this study can be found in the supplementary material. The WDI data used in this study span from 2000 to 2017.The outcome variable of interest in this study was under-five mortality rate per 1,000 live births. The national estimates for under-five mortality rates were compiled by the UN Inter-agency Group for Child Mortality Estimation (UN IGME) which consists of the following international organisation: the United Nations Children’s Fund (UNICEF), the World Health Organization (WHO), the World Bank and the Population Division of the United Nations’ Department of Economic and Social Affairs (UN DESA Population). The UN IGME under-five mortality rate estimates are based on nationally representative data, including data sourced from household surveys and censuses. To ensure quality data (estimates), the UN IGME excludes data sources with substantial non-sampling errors or omissions from the statistical model used to derive the estimates. The UN IGME estimation process does not use covariates and a detailed description of the modelling process can be found elsewhere .Percentage of females with secondary educationPercentage of females aged 15 years and above employed as a ratio of the total populationGross domestic product—GDP—per capita (current US$)Gross national expenditure as a percentage of GDPPercentage of out-of-pocket expenditure out of current health expenditurePercentage of people using at least basic drinking water servicesPercentage of the population practising open defecationPercentage of population using at least basic sanitation servicesMean annual PM2.5 exposure in micrograms per cubic meterEvidence from existing studies highlight correlation between national socioeconomic development and child health –33. The First, we examined the association between the world development indicators used in this study and under-five mortality rate per 1000 live births. Considering the temporal dimension of the data, we employed Bayesian Spatio-temporal modelling to assess the effect of the development indicators on under-five mortality. Several space-time interaction Bayesian models for disease risk and mapping have been proposed in existing literature . These m average , 38For the non-parametric models, five models with different space-time interaction specifications were built. The first non-parametric model (Model 2) is similar to Bernardinelli et al.  proposedmentclass2pt{minimThe remaining four models are extensions of Model 2 that allow four different types of interactions between space and time as proposed by Knorr-Held (2000). The first space-time interaction (TYPE I) is based on the assumption of unstructured space-time effect; that is, neighbouring spatial units do not affect each other and mortality rates of previous years do not affect that of subsequent years—Model 3. The second space-time interaction (TYPE II) assumes an interaction between a structured temporal effect and an unstructured spatial effect—Model 4. The third space-time interaction (TYPE III) is based on the assumption of an interaction between an unstructured temporal effect and a spatially structured effect—Model 5. Finally, the fourth space-time interaction (TYPE IV) is a complex interaction model that assumes an interaction between spatially and temporally structured effects—Model 6. A detailed discussion of these four space-time interactions and their implementations are described elsewhere , 38, 39.i = 1, 2 …, n and t = 1,2,3, …, T; and g(.) is the appropriate link function. Next, a Bayesian spatiotemporally varying coefficient  model to explore spatial and temporal variations in the relationships between the development indicators and under-five mortality rates. Compared to traditional Spatio-temporal models, STVC models move beyond the naïve assumption that the association between predictors and outcome are the same across space and time. It also accounts for autocorrelation in the spatial and temporal dimension of the data; that is, neighbouring units have the potential of affecting the outcome in a unit and mortality rates of previous time periods affect the rate of successive periods. The Bayesian STVC model can be defined simply as:lsewhere .Both Spatio-temporal and Spatiotemporally varying coefficients models were implemented using the integrated nested Laplace approximations (INLA) framework with the PARDISO sparse matrix library for high-performance computing . INLA isFor the Spatio-temporal models, we compared the results of the Bernardinelli et al.  parametr−3. Additional file Table This section presents the results of the parametric temporal trend and the Type IV space-time interaction models. The estimates of the fixed-effect component of the model for the parametric temporal trend model  and Type IV space-time interaction model (Model 6) are shown in Table Figure The differential trend for the space-time interaction for Model 6 for selected years is shown in Fig. 2.5 increase under-five mortality rates. Similarly, figures for the other years—2005, 2010 and 2017—show spatial variations in the effect of the development indicators on under-five mortality across the world . This is because the space-time interaction is not orthogonal to the fixed effects; in other words, the structure of the assumed space-time interaction is correlated with the model predictors—development indicators. This suggests spatial and temporal dependence in the association between the WDIs and under-five mortality rate. That is, the result suggests the under-five mortality rate of a country is influence by neighbouring countries and by previous mortality rates. In the Type IV interaction model (Model 6) we observed an increase in the proportion of employed females is associated with increased under-five mortality. One plausible explanation for this is that females compared to their male counterparts often work in unpaid employment, and precarious jobs and earn less wage , 56. Thu2.5 exposure is associated with a reduction in under-five mortality. The finding is not surprising; albeit, it contradicts the evidence of existing studies that show that a higher level of mean annual PM2.5 exposure is associated with higher under five or child mortality [2.5 exposure measurement could be indicative of socioeconomic development [The findings also show that when we assume Type IV space-time interaction, an increase in mean annual PMortality , 8, 57. elopment , 59. Exielopment . SocioecWe also observed mixed results for the effect of current health expenditure as a percentage of GDP on under-five mortality. In the parametric time-trend model, an increase in the percentage of current health expenditure was associated with a reduction in under-five mortality. In the non-parametric temporal model with Type IV space-time interaction, current health expenditure as a percentage of GDP was associated with an increase in under-five mortality. These results indicate conceptions of the type of space-time interaction can influence the nature of the association between some development indicators and under-five mortality.The findings for spatial effect parameters for the parametric time-trend model show that the country-specific effect on under-five mortality . Another key strength of this study is the use of spatiotemporal modelling to understand the spatial and temporal patterns of the associations between socioeconomic development and environmental indicators and under-five. To the best of our knowledge, this study is the first to explore spatiotemporal variations in the associations between the development indicators and under-five mortality in a global context. A major limitation of this study is the geographic unit of analysis. The use of countries as spatial units of analysis ignores spatial differences in socioeconomic development and child mortalities within these countries. However, the curated data only exist at the national level hence the authors could use lower administrative units as geographic units of analysis. Another limitation is that the data used in this study covers 18 years period and did not include data from the past five years (due to the unavailability of the data for some indicators).The findings of this study show that the development indicators of countries are associated with under-five mortality rates. From the finding, we observed that indicators of good socioeconomic development such as female education, GDP, current health expenditure access to drinking water and sanitation were associated with a reduction in under-five mortality rates. On the other hand, open defecation—an indicator of underdevelopment—was associated with higher under-five mortality rates. The findings also indicate the trend of under-five mortality rates in the sub-Saharan African region is less steep compared to the global average. Given there has been a general decline in under-five mortality rates, this result shows the trend in reduction in the sub-Saharan African region has been slower than the global trend. The burden of under-five mortality rates is considerably higher among sub-Saharan African countries and some southern Asian countries as evident in the result of the country-specific effects of under-five mortality. Investment in female education, health expenditure and access to basic social amenities (water and sanitation) through sound macroeconomic policies in these high burden countries can help reduce global under-five mortality rates.Additional file 1: Table S1. Summary information of countries included in this study. Table S2. Proportion of missingness and number of data points . Table S3. Diagnostics information for space-time interaction model selection. Figure S1. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2000 showing countries with significant observed effects . EDU ); EMP  (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE ); OHP ); HEXP ); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM ). Figure S2. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2005 showing countries with significant observed effects . EDU ); EMP  (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE ); OHP ); HEXP ); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM ). Figure S3. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2010 showing countries with significant observed effects . EDU ); EMP  (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE ); OHP ); HEXP ); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM ). Figure S4. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2017 showing countries with significant observed effects . EDU ); EMP  (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE ); OHP ); HEXP ); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM ). Table S4. Number of countries with missing information from 1960 to 2019 by study variables Number of countries with missing data for all available years by the study variables.


In this issue of the JCI, Zhang, Yan, and co-authors demonstrate that increased numbers of functional inhibitory KIR-HLA pairs equating to greater negative regulation promoted longer lifespans of human T cells. This effect was independent of direct signals provided to KIR-expressing T cells and was instead driven by indirect mechanisms. Since the long-term maintenance of CD8+ T cells is critical for immune readiness against cancer and infection, this discovery has implications for immunotherapy and the preservation of immune function during aging.Killer immunoglobulin-like receptors (KIRs) are polymorphic receptors for human leukocyte antigens (HLAs) that provide positive or negative signals controlling lymphocyte activation. Expression of inhibitory KIRs by CD8 Killer cell immunoglobulin-like receptors (KIRs) are a family of polymorphic activating and inhibitory receptors with specificity for various class I HLA receptors. Inheritance of certain combinations of KIR and HLA genes is implicated in susceptibility to infections, autoimmune disease, and cancers . Althoug+ T cells. The frequencies of KIR+CD8+ T cells increase with age and become elevated in the contexts of chronic infection and autoimmunity  or KIR3DL1 (HLA-B). In contrast to previous reports  as CD8+ T cells from individuals with only two KIR-HLA pairs (125 days). The number of KIR-HLA pairs was also linked to the accumulation of CD8+ T cells bearing markers of advanced cellular age such as CD57. Functional engagement of inhibitory KIRs by HLA ligands was essential for this effect, as counting the total number of inhibitory KIRs without regard to the presence of cognate HLA substantially diminished the predictive power for T cell lifespan. Thus, increased negative KIR signaling within the immune system indirectly favors longer lifespans for CD8+ T cells  to enhance NK cell killing of tumor cells are being tested in clinical trials (Protective subsets of memory CD8esponses . Moreovel trials . SimilarHAF is listed as first co–first author, as he provided a slight majority of the text, while HC is listed a second co–first author, as she took the lead in preparing the artwork and describing the clinical relevance of the findings.


These results suggest that cross-frequency functional connectivity network topology may be a non-invasive biomarker of SUDEP risk.Sudden unexpected death in epilepsy (SUDEP) is the leading seizure-related cause of death in epilepsy patients. There are no validated biomarkers of SUDEP risk. Here, we explored peri-ictal differences in topological brain network properties from scalp EEG recordings of SUDEP victims. Functional connectivity networks were constructed and examined as directed graphs derived from undirected delta and high frequency oscillation (HFO) EEG coherence networks in eight SUDEP and 14 non-SUDEP epileptic patients. These networks were proxies for information flow at different spatiotemporal scales, where low frequency oscillations coordinate large-scale activity driving local HFOs. The clustering coefficient and global efficiency of the network were higher in the SUDEP group pre-ictally, ictally and post-ictally ( Sudden unexpected death in epilepsy (SUDEP) is the leading cause of epilepsy-related mortality, however, the etiology remains poorly understood . Fear ofSeveral clinical factors correlate with SUDEP risk. A few of these, such as occurrence and frequency of generalized tonic-clonic and other types of seizures over the preceding year, duration of epilepsy, and use of multiple anti-seizure medications, among others , have beFunctional brain networks reflect the complex interactions in the brain and may distinguish pathology from normal functioning brain. These rhythms are important in information processing in the brain, with low frequencies being more spatially distributed and responsible in coordinating local high frequency activity. In this study, we aim to compare peri-ictal network differences in SUDEP patients using graph theory measures of directed functional connectivity. Specifically, we construct novel directed graphs combined from delta - HFO functional connectivity to capture the cross-frequency interactions between different brain regions. We use these directed graphs and their topologies to discern between SUDEP and non-SUDEP epileptic patients.Scalp EEG recordings for 14 non-SUDEP  and 8 SUDEP  patients were provided through a consortium formed by the Toronto Western Hospital, the NYU Comprehensive Epilepsy Center, and the Phramongkutklao Royal Army Hospital . Non-SUDPatient data were originally filtered with a 0.1 Hz high pass filter during acquisition and were later pre-processed by removing power line interference using a finite impulse response (FIR) notch filter at 50 Hz or 60 Hz (data centre location dependent) and associated harmonics. Recordings used an acquisition reference at FCz, grounded at Fpz. Computations were performed on the Niagara supercomputer at the SciNet HPC Consortium. SciNet is funded by: the Canada Foundation for Innovation; the Government of Ontario; Ontario Research Fund - Research Excellence; and the University of Toronto .Wavelet phase coherence (WPC) was computed between pairs of scalp EEG electrodes. The phase of different frequency bands was extracted through the complex wavelet transform . The MorWPC was applied to each wavelet central frequency in the delta (0.5–2 Hz) and HFO (80–120 Hz) ranges, in increments on a logarithmic base two scale. Wavelet frequency scales took the form of The undirected connectivity matrices were computed at 1 s intervals by assigning each edge corresponding to a pair of scalp electrodes to the WPC averaged over the frequency range (delta or HFO) and over 1 s temporal windows. The edges between electrodes are undirected, yielding to symmetrical connectivity matrices.The undirected connectivity matrices were derived from delta–HFO WPC. The degree of each node was computed for both the delta and HFO WPC connectivity adjacency matrices to construct the directed delta-HFO network. Edges between pairs of nodes activity . The natactivity  with claactivity . Additioactivity , where lGlobal coherence was computed as a measure for the undirected delta and HFO connectivity networks. First the eigenvalues of the connectivity matrix were computed and sorted. The global coherence is a ratio between the largest eigenvalue to the sum of all eigenvalues and has been previously used to analyze spatiotemporal EEG dynamics .CGlobalTwo network measures were computed: the clustering coefficient and the global efficiency. Together, these measures provide a description of the connection topology in the brain network.The clustering coefficient for each node/electrode node i . The cluThe global efficiency of the network is a measure which quantifies how information flows throughout the network. Graphs with a high global efficiency have on average shorter paths connecting any two nodes within the network. The global efficiency was used instead of the characteristic path length as the shortest path length is not defined when a network contains two nodes that are not connected by any path. The global efficiency is the average efficiency, defined as the inverse of the shortest path between two nodes, over all electrode pairs .E=1n and non-SUDEP (P1) patients . The grap = 0.00100, ictal: p = 0.00001, post-ictal: p = 0.00100). The global efficiency, which indicates how efficiently information is transferred between nodes, was shown to be significantly higher in the SUDEP group pre-ictally, ictally, and post-ictally . These results show that network topology is a potential biomarker in assessing SUDEP risk.The clustering coefficient and global efficiency measures were used to compare group differences between non-SUDEP and SUDEP epileptic patients . The cluWe have found topological differences in the peri-ictal delta-HFO directed networks of epileptic patients with SUDEP exhibiting significantly higher pre-ictal, ictal, and post-ictal clustering coefficient and global efficiency in the delta-HFO directed networks. These data suggest a higher connectivity and more efficient flow of information in seizure networks of SUDEP patients. Both high clustering coefficient and high global efficiency are features that resemble a small-world organized network as first described by Watts and Strogatz . These nThe delta-HFO directed networks captured the complexity of the seizure networks and differences between SUDEP and non-SUDEP groups. The importance of these rhythms is consistent with previous studies localizing seizure networks . A recenProposed mechanisms of SUDEP involve ictal-related cardio-respiratory dysfunction, which may be caused by epileptiform activity spreading to the brainstem. A crucial element of SUDEP is brainstem dysfunction, for which PGES might be a biomarker . The MORFurther research needs to be done using intracranial EEG in both patient groups to have a deeper understanding of how these topological changes relate to seizure spread and brainstem dysfunction. A limitation of this study is the low number of patients in the SUDEP group. More patients need to be added to the sample size to examine the predictive power of this biomarker. Although the network dynamics throughout representative seizures seemed to differ in SUDEP patients, further exploration needs to be done in comparing group differences. Regarding patient selection criteria, EEG recordings in this study were obtained from patients monitored in the EMU. While most patients were weaned off anti-seizure medications in order to provoke seizures, changes in their medication regimen were not annotated in the EEG recordings. This should be taken into consideration for future studies. Also, all non SUDEP patients were medically refractory and this may limit the generalizability of this biomarker to the broader epilepsy population. In conclusion, there is an unmet need for non-invasive biomarkers to identify those patients at high risk for developing seizure-associated SUDEP. Our study describes such a biomarker for SUDEP using scalp EEG signals to construct functional connectivity networks of the brain.


Sargın2 As we discussed in the manuscript, this was one of the main limitations of our real-life retrospective study. Nevertheless, our study population was one of the largest samples of HBsAg-negative and anti-HBc IgG-positive patients exposed to several biologic agents. Despite this limitation, we believe that it makes an important contribution to the literature.She raised concerns that the absence of follow-up hepatitis B virus (HBV) DNA levels in a small group of patients may cause an underestimation of the hepatitis B reactivation rate in our study. We agree with the author about the importance of HBV DNA monitoring at least 3-4 times in the first year.3 Therefore, we cannot rule out the possibility of occult HBV infection with HBV DNA testing. The EASL 2017 clinical practice guidelines on managing HBV infection indicate that the HBsAg-negative and anti-HBc IgG-positive phase (phase 5) of chronic HBV infection may also be named “occult HBV infection.”4 It is unclear whether the presence of low levels of HBV DNA in the serum increases the risk of HBV reactivation under biological treatment. We believe that the absence of baseline HBV DNA measurements does not weaken the study’s results. If subjects with detectable HBV DNA cases were diagnosed and excluded from the study, the reactivation rate would be more likely to decrease than increase.In the letter, the author emphasized that three-quarters of the study population were not screened for the presence of HBV DNA prior to biologic exposure. She also pointed out the probability of occult hepatitis B infection in our population. Occult HBV infection is a challenging clinical entity defined as the presence of replication-competent viral DNA in the liver of HBsAg negative individuals with or without detectable HBV DNA in the serum.Finally, we agree with the author that in clinical practice, HBV DNA should be tested before biological exposure in these patients. Pre-emptive therapy seems the safest and most cost-effective way to prevent HBV reactivation in HBsAg-negative and anti-HBc IgG-positive patients under biologic treatment.


Retinal degenerative diseases such as age-related macular degeneration (AMD) represent a leading cause of blindness, resulting in permanent damage to retinal cells that are essential for maintaining normal vision. Around 12% of people over the age of 65 have some form of retinal degenerative disease. Whilst antibody-based drugs have revolutionised treatment of neovascular AMD, they are only effective at an early stage and cannot prevent eventual progression or allow recovery of previously lost vision. Hence, there is a clear unmet need to find innovative treatment strategies to develop a long-term cure. The replacement of damaged retinal cells is thought to be the best therapeutic strategy for the treatment of patients with retinal degeneration. Advanced therapy medicinal products (ATMPs) are a group of innovative and complex biological products including cell therapy medicinal products, gene therapy medicinal products, and tissue engineered products. Development of ATMPs for the treatment of retinal degeneration diseases has become a fast-growing field of research because it offers the potential to replace damaged retinal cells for long-term treatment of AMD. While gene therapy has shown encouraging results, its effectiveness for treatment of retinal disease may be hampered by the body’s response and problems associated with inflammation in the eye. In this mini-review, we focus on describing ATMP approaches including cell- and gene-based therapies for treatment of AMD along with their applications. We also aim to provide a brief overview of biological substitutes, also known as scaffolds, that can be used for delivery of cells to the target tissue and describe biomechanical properties required for optimal delivery. We describe different fabrication methods for preparing cell-scaffolds and explain how the use of artificial intelligence (AI) can aid with the process. We predict that combining AI with 3D bioprinting for 3D cell-scaffold fabrication could potentially revolutionise retinal tissue engineering and open up new opportunities for developing innovative platforms to deliver therapeutic agents to the target tissues.  Age-related macular degeneration (AMD) is a progressive, multifactorial neurodegenerative disease of the macula and is a leading cause of irreversible blindness, affecting 1 in 8 people 60 years of age or older in the Western world . AMD is AMD is further classified into two groups based on the absence or presence of neovascularisation: non-exudative or non-neovascular (‘dry’) AMD and exudative or neovascular (‘wet’) AMD . Wet AMDThe aim of this article is to provide an up-to-date review of recent progresses in development of advanced therapy medicinal products (ATMPs) for the treatment of AMD, including both scaffold and non-scaffold-based approaches. The properties of a scaffold such as porosity, permeability, biocompatibility, and fabrication methods have a major influence on the control of cell migration and growth . TherefoSeveral factors are believed to play a role in the pathogenesis of dry AMD including genetics, oxidative stress, inflammatory , environRPE is a monolayer of non-regenerative polygonal cells functioning as a barrier between the underlying Bruch’s membrane/choriocapillaris complex and the photoreceptors . The RPEDrusen contains several proinflammatory factors such as complement components and tumour necrosis factors (TNF-α) . ActivatAccumulation of ROS results in overproduction of lipofuscin and -amyloid . A vicio-amyloid . Anti-in-amyloid . Furthermore, clinical studies have demonstrated that dietary antioxidants including zinc, carotenoids, flavonoids, and resveratrol, may serve as potential therapeutic interventions to prevent damage induced by ROS or even reverse vision loss . As descExperimental evidence on different ω3 Polyunsaturated fatty acids (PUFAs) has documented the beneficial effects of docosahexaenoic acid (DHA) in modulating antioxidant gene expression in RPE cells under high glucose-like conditions . In anotIdebenone (IDB) has anti-apoptotic and cytoprotective effects on RPE cells under oxidative stress conditions, as confirmed by in vitro studies. These studies suggest that pre-treatment with IDB mediates the overexpression of Nrf-2 by increasing the level of Bcl-2 and decreasing the level of mitochondrial ROS levels, thus protecting against oxidative damage . OT-551 ®), aflibercept  and brolucizumab  and faricimab . Bevacizumab, anti-VEGF full antibody, has also been used off-label to treat wet-AMD [The thickness of the Bruch’s membrane and the associated hypoxic conditions lead to overexpression of VEGF, which can give rise to angiogenesis and neovascularisation . Current wet-AMD . Althoug wet-AMD . Current wet-AMD .To summarise, several therapeutic approaches are being investigated focusing on: (1) disease prevention, including antioxidant and visual cycle modulators, (2) halting disease progression, including drugs with anti-inflammatory, oxidative stress, mitochondrial enhancer, β-amyloid inhibitor and neuroprotective properties, and/or (3) vision restoration, including cell and gene therapy. While there are extensive reviews describing therapeutic options to address (1) and (2) in detail, which are listed in Although various pharmacological treatments are available for the treatment of AMD, the ideal treatment still does not exist. Advanced therapy medicinal products (ATMPs) are a group of complex and innovative biological products made for human use, consisting of cell therapy medicinal products (CTMPs), gene therapy medicinal products (GTMPs) and tissue engineered products (TEPs) . The eyeCell therapy medicinal products are being developed to treat AMD by exploiting the immune-privileged environment of the subretinal space. Two mechanisms have been suggested, including replacing or regenerating the damaged RPE cells and introducing cells that can exert a supportive paracrine effect on photoreceptor function and survival ,58. CurrTransplantation of healthy RPE cells seeded in carefully crafted biomimetic scaffolds can better mimic morphology of native tissues and aid in the restoration of visual function . The ide®, in Phase1/2a for patients with progressive dry-AMD, which composed of RPE cells derived from human embryonic stem cells. The therapy is administered as a cell suspension in a single surgery to the subretinal space [® can be delivered to the subretinal region, and to evaluate the safety of the procedure [A number of clinical trials are currently underway to evaluate the effectiveness of cell-based therapy for the treatment of AMD. One such therapy is OpRegenal space . The prirocedure . Anotherrocedure . Howeverrocedure . Additiorocedure . Human urocedure . Surgicarocedure . Gene therapy medicinal products (GTMPs) have also attracted the attention of researchers in the field of ocular therapeutics. Gene therapy involves introducing healthy genes to replace non-functioning or deficient proteins in patients’ cells using biological gene delivery vehicles or “vectors”. The therapeutic genes can be injected underneath the retina or directly into the vitreous body . Gene-baA biotechnology company called SparingVision is currently developing SPVN06, an AAV-based gene therapy product that could potentially counteract the degeneration of cone photoreceptors in dry age-related AMD by restoring RdCVF, a neurotrophic factor secreted by photoreceptor cells in the retina, and by promoting RdCVFL, a potent antioxidant which protects retinal cone cells against damage caused by oxidative stress . SparingWhilst gene therapy represents an ideal option for treating a debilitating condition like AMD, a number of challenges still remain in identifying therapeutic targets and designing efficient gene delivery vectors. The route of vector administration is also a major determinant of the efficacy of gene therapies . Three pSeveral early-phase clinical trials have shown promising results, however, translating gene and cell therapies from the laboratory to the clinic remains a significant challenge. While gene therapies could potentially have a substantial impact on the treatment of AMD, their long-term effectiveness has been hindered by the body’s response and issues related to ocular inflammation which could lead to vision loss. Currently, AAV-based gene therapies are limited to targeting long-lived cells . MoreoveAlthough cell and gene therapies have demonstrated great promise, their translation from bench to bedside remains a significant challenge. Tissue engineering (TE) offers an alternative approach for treating AMD by developing tissue engineered products (TEPs) that can replace dysfunctional RPE and restore its function, thus halting the progression of the disease . TE for A thorough understanding of fundamental scaffold characteristics and functions is crucial for developing an effective scaffold fabrication strategy. The type of biomaterial used for scaffold fabrication is another important parameter in scaffold design. The selection of biomaterials plays a key role in determining the biomechanical properties of the scaffold and its ability to interact with native tissues in a biologically appropriate manner, rather than acting as an ‘inert body’ . NaturalVarious techniques have been developed for manufacturing 3D scaffolds, including electrospinning, freeze-drying and 3Dbioprinting . The eleThe freeze-drying (lyophilization) method is another commonly used technique in regenerative medicine for creating water-soluble polymer scaffolds through the process of sublimation . The proMore recently, 3D bioprinting has shown great potential in tissue engineering for scaffold fabrication, by integrating living cells (bioink) and biomaterials directly or indirectly together using layer-by-layer approach . To dateThe human retina is a highly complex tissue that serves as a key component of both the eye and central nervous system. Any damage to retinal cells including photoreceptor cells, can lead to permanent vision loss because the retina lacks an endogenous stem cell population to replace degenerated and/or damaged cells. AMD is a retinal degenerative disease that affects around 20 million people in the US alone, and it is projected that approximately 300 million worldwide will have the disease by 2040. Although treatment options exist for the neovascular form of AMD to prevent and halt progression of the disease, opportunities for restoring vision are limited once photoreceptor cells become dysfunctional. Advanced therapies represent an emerging field of research which seek to develop cell- and gene-based therapies to treat and potentially cure degenerative diseases such as AMD. Different products for both cell- and gene-based approaches are currently in clinical trials for treatment of both AMD and GA, however, no product has so far reached the market and received FDA approval. This must be due to challenges in selecting the appropriate gene and/or cell types, designing suitable delivery vehicles and ensuring ocular tolerability. Natural polymers also have their own limitations in terms of processability and mechanical properties. Changes in the constituents of natural polymers can lead to accumulation of debris in Bruch’s membrane and eventual cell death.Recently, scaffold-based approaches for treating retinal degenerative diseases have provided a promising avenue to halt or prevent disease progression in AMD. Nevertheless, different biomaterials and methods of scaffold fabrication can have a significant impact on product success. The freeze-drying method is a primary step that is simple and convenient for making porous structures but has limitations such as (i) poor reproducibility, (ii) poor mechanical properties and (iii) less controllable microstructures and properties. Electrospinning technology has the ability to control fibre diameter and provide a high surface area for cell attachment. However, this method requires the use of toxic organic solvents during processing which can have an adverse effect on cell viability. 3D-bioprinting represents a more attractive scaffold fabrication method with potential advantages of superior physical characteristics, cell adhesion, better control of scaffold microstructures and overall improved mechanical properties, compared to freeze drying.Multi-objective optimisations are, however, required to identify suitable printing conditions for 3D bioprinting  and material composition (percentage of cells to bioink) to achieve optimal mechanical and porous constructions properties. This requires extensive experimentation time that is resource-demanding. Artificial intelligence (AI) and machine learning (ML) methods have already been applied to solve problems in scientific research and represent potentially transformative resources to support researchers in the field of regenerative medicine. In the context of 3D bioprinting, an ML-based supervised learning framework could take the material composition and the printing parameters as input to (i) to enable optimisation of scaffold properties, (ii) assess the quality of the prints and (iii) optimise printability of the material. Given the appropriate training datasets, a machine learning algorithm could generate an accurate prediction model for efficient scaffold fabrication and provide a quantitative evaluation of scaffold printability for specific anatomical sites, such as the human retina. We predict that combining AI with 3D bioprinting for 3D cell-scaffold fabrication will revolutionise retinal tissue engineering and open up a world of new opportunities for developing novel drug delivery platforms for the treatment of retinal diseases.