Impact of genetic polymorphisms on the degree of ovarian response to gonadotrophin stimulation in patients undergoing ICSI treatment

Abstract: Infertility is a common health problem associated with various medical, emotional, and social implications. It affects one in four couples in developing countries and one in six couples worldwide at least once during their reproductive lifetime. Intracytoplasmic sperm injection (ICSI) is the most common technique of assisted reproduction, accounting for approximately three-quarters of all infertility treatments worldwide. Despite the availability of new ovarian reserve markers and improvements in the methodologies that support personalization of In vitro fertilization (IVF) treatment protocols, an accurate definition of the modalities for applying personalized therapy to optimize efficacy and daily clinical management is still required. Genetic differences between patients are most likely the main factor responsible for different responses to the drugs. The gonadotrophin hormones, follicle-stimulating hormone (FSH) and luteinising hormone (LH), control folliculogenesis, and naturally occurring polymorphisms in genes encoding these hormones and their receptors may affect the ovarian response. However, a definite association between genetic polymorphisms and ovarian responses to gonadotrophins still needs to be determined. The purpose of this study was to detect the association between five single nucleotide polymorphisms of the following four genes follicle-stimulating hormone receptor (FSHR), anti-Mullerian hormone (AMH), luteinizing hormone/choriogonadotropin receptor (LHCGR), estrogen receptor (ESR1), and the degree of the ovarian response to gonadotrophin in Egyptian Patients undergoing IVF/ICSI therapy. The study population was Egyptian Women undergoing ICSI treatment. Two hundred and eighty women have participated in the study with mean aged 20 -35 years old. The clinical part of the study was performed in the IVF unit Sohag, Egypt starting with patient recruitment and selection. Preparatory phase and investigations before ICSI then Controlled ovarian stimulation (COS) by Long Gonadotrophin releasing hormone (GnRH) agonist protocol, patient follow-up, and samples collection. The patients were classified according to ovarian response into three groups: normal responders (retrieved oocytes=4-15) (n= 80), poor responders (retrieved oocyte 15) (n= 108). Approximately 5.0 ml of blood samples were collected from all participants in EDTA tubes and stored at -80°C until the genetic analysis to be performed in assisted reproductionand Genetics Unit, Saarland University, Germany. Genomic DNA was extracted from the blood samples, and the PCR and DNA sequencing were performed to compare the variation in the DNA sequencing between the different study groups. The quantitative PCR (qPCR) was performed to evaluate the expression level of the following genes: FSHR, AMH, LHCGR, ESR1, and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a reference gene among the study groups. Data analysis was performed by SPSS software. The Kruskal–Wallis (H-test) and Mann-Whitney (U-test) were applied to compare the median quantitative variables between the study groups. The Spearman rank correlation was used to evaluate the association between genetic polymorphisms (rs4073366, rs6166, rs6165, rs2234693; rs17854573) and the different investigated parameters including clinical parameters e.g., age, Body mass index (BMI), hormonal parameters e.g., AMH level, FSH Level and ICSI cycle parameters e.g., dose of gonadotrophin, number of collected oocytes, number of fertilised oocytes and number of transferred embryos. Regarding the qPCR data, the comparative analysis was used to calculate the expression level of tested genes in the cases “poor/high responder” versus controls “normal responder”. The results were considered statistically significant when P-value ≤ 0.05. The data analysis of DNA sequencing showed a significant difference in the frequency of the following genotypes FSHR (rs6166), AMH (rs17854573), and ESR1 (rs2234693) in the poor responders compared to normal responders (P ≤ 0.001, P = 0.010, and P ≤ 0.001) respectively. No significant difference has been found in the frequency of LHCGR (rs4073366) and FSHR (rs6165) genotypes in patients with poor ovarian response compared to others with a normal ovarian response (P = 0.312 and P = 0.192). Besides, no significant difference has been found in the frequency of the FSHR (rs6166), FSHR (rs6165), ESR1 (rs2234693), AMH (rs17854573) or LHCGR (rs4073366) genotypes in high responders compared to normal responders (P = 0.074, P = 0.353, P = 0.060, P = 0.060 and P = 0.091 respectively). Moreover, a significant difference has been found between the poor responders and the normal responders in the total dose of gonadotropin, the number of stimulation days, the number of collected oocytes, the number of injected oocytes, the number of fertilized oocytes, and the number of embryo transfer.On the other hand, the analysis of qPCR results revealed a variation between the study groups (poor, normal, and high response) in the expression levels of FSHR (rs6166), FSHR (rs6165), AMH, LHCGR, and ESR1 gene (P ≤ 0.0001). In conclusion, The results of this study suggest that polymorphisms in the genes for key reproductive hormones (AMH, FSHR, and ESR1) in combination with the patient’s clinical characteristics and hormonal biomarkers could be used to predict the ovarian response to gonadotrophins, to personalize and adjust the dose of gonadotrophins before starting the stimulation protocol, to improve efficacy and to avoid possible complications such as cycle cancellation and OHSS; and, finally, to improve the pregnancy rate in patients undergoing ICSI treatment.Zusammenfassung: Infertilität ist ein häufiges Gesundheitsproblem, das mit verschiedenen medizinischen, emotionalen und sozialen Auswirkungen einhergeht. Es betrifft eines von vier Paaren in Entwicklungsländern und eines von sechs Paaren weltweit mindestens einmal im Laufe ihres reproduktiven Lebens. Die intrazytoplasmatische Spermieninjektion (ICSI) ist die häufigste Technik der assistierten Reproduktion und macht weltweit etwa drei Viertel aller Unfruchtbarkeitsbehandlungen aus. Obwohl neue Marker für die ovarielle Reserve zur Verfügung stehen und die Methoden zur Personalisierung der Behandlungsprotokolle für die In-vitro-Fertilisation (IVF) verbessert wurden, ist eine genaue Definition der Modalitäten für die Anwendung der personalisierten Therapie zur Optimierung der Wirksamkeit und des täglichen klinischen Managements nach wie vor erforderlich. Genetische Unterschiede zwischen den Patienten sind höchstwahrscheinlich der Hauptfaktor, der für die unterschiedlichen Reaktionen auf die Medikamente verantwortlich ist. Die Gonadotropin Hormone FSH und luteinisierendes Hormon (LH) steuern die Follikulogenese, und natürlich vorkommende Polymorphismen in Genen, die für diese Hormone und ihre Rezeptoren kodieren, können die Reaktion der Eierstöcke beeinflussen. Ein eindeutiger Zusammenhang zwischen genetischen Polymorphismen und der Reaktion der Eierstöcke auf Gonadotropine muss jedoch erst noch festgestellt werden. Ziel dieser Studie war es, den Zusammenhang zwischen fünf Einzelnukleotid-Polymorphismen der folgenden vier Gene Follikel-stimulierendes Hormon-Rezeptor (FSHR), Anti-Mullerian-Hormon (AMH), Luteinisierendes Hormon/Choriogonadotropin-Rezeptor (LHCGR), Östrogenrezeptor (ESR1) und dem Grad der ovariellen Reaktion auf Gonadotropin bei ägyptischen Patientinnen, die sich einer IVF/ICSI-Therapie unterziehen, zu ermitteln. Die Studienpopulation bestand aus ägyptischen Frauen, die sich einer ICSI-Behandlung unterzogen. Zweihundertachtzig Frauen, mit einem Mittelalter von 20 bis 35 Jahren, nahmen an dieser Studie teil. Der klinische Teil der Studie wurde in der IVF-Abteilung in Sohag, Ägypten durchgeführt. Zuerst erfolgten die Rekrutierung und Auswahl der Patientinnen, dann die Vorbereitungsphase und Untersuchungen vor ICSI danach die kontrollierte ovarielle Stimulation (COS) durch langwirkende GnRH-Agonisten-Protokoll.Anschließend erfolgte das Follow-up. Nach der ovariellen Reaktion wurden die Patientinnen in drei Gruppen eingeteilt: normale Responder (entnommene Eizellen = 4-15) (n = 80), schlechte Responder (entnommene Eizellen ) 15) (n = 108). Ca. 5,0 ml Blutproben wurden von allen Teilnehmerinnen in EDTA-Röhrchen gesammelt und bei -80° C, bis die genetische Analyse in der Abteilung von der assistierten Reproduktion und Genetik an der Universität des Saarlandes in Deutschland, gelagert. Genomische DNA wurde aus den Blutproben extrahiert und die PCR und die DNA-Sequenzierung wurden durchgeführt, um die Variation in der DNA- Sequenzierung zwischen den verschiedenen Studiengruppen zu vergleichen. Die quantitative PCR (qPCR) wurde durchgeführt, um das Expressionsniveau der folgenden Gene zu bewerten: FSHR, AMH, LHCGR, ESR1 und Glyceraldehyd-3-phosphat-Dehydrogenase (GAPDH) als Referenzgen in den Studiengruppen. Die Datenanalyse wurde mit der Software SPSS durchgeführt. Der Kruskal-Wallis-Test (H-Test) und der Mann-Whitney-Test (U-Test) wurden angewandt, um den Median der quantitativen Variablen zwischen den Studiengruppen zu vergleichen. Die Spearman-Rangkorrelation wurde verwendet, um den Zusammenhang zwischen dem genetischen Polymorphismus (rs4073366, rs6166, rs6165, rs2234693; rs17854573) und den verschiedenen untersuchten Parametern zu bewerten einschließlich klinischer Parameter wie Alter, Body-Mass-Index (BMI), hormoneller Parameter wie AMH-Spiegel, FSH-Spiegel und ICSI-Zyklusparameter wie Gonadotropin-Dosis, Anzahl der entnommenen Eizellen, Anzahl der befruchteten Eizellen und Anzahl der übertragenen Embryonen. In Bezug auf die qPCR-Daten wurde die vergleichende Analyse verwendet, um das Expressionsniveau der getesteten Gene in den Fällen "schlechte/hohe Responder" gegenüber den Kontrollen "normale Responder" zu berechnen. Die Ergebnisse wurden als statistisch signifikant angesehen, wenn der P-Wert ≤ 0,05 war. Die Datenanalyse der DNA-Sequenzierung zeigte einen signifikanten Unterschied bezüglich der Häufigkeit der folgenden Genotypen FSHR (rs6166), AMH (rs17854573) und ESR1 (rs2234693) bei den schlechten Respondern im Vergleich zu den normalen Respondern (P ≤ 0,001, P = 0,010 und P. ≤ 0,001). Es wurde kein signifikanter Unterschied in der Häufigkeit der Genotypen von LHCGR (rs4073366) und FSHR (rs6165) bei den schlecht ansprechenden Respondern im Vergleich zu den normalen Respondern beobachtet (P ≤ 0,312 und P = 0,192).Außerdem wurde kein signifikanter Unterschied in der Häufigkeit der Genotypen FSHR (rs6166), FSHR (rs6165), ESR1 (rs2234693) , AMH (rs17854573) oder LHCGR (rs4073366) bei den High-Respondern im Vergleich zu den normalen Respondern (P ≤ 0,074) gefunden , P ≤ 0,35 3, P ≤ 0,060, P ≤ 0,060 bzw. P ≤ 0,091 ). Darüber hinaus wurde ein signifikanter Unterschied zwischen den schlechten Respondern und den normalen Respondern bezüglich der Gesamtdosis von Gonadotropin, der Anzahl der Stimulationstage, der Anzahl der gesammelten Eizellen, der Anzahl der injizierten Eizellen, der Anzahl der befruchteten Eizellen und der Anzahl der gefunden Embryotransfer sowie die Anzahl der kryokonservierten Embryonen. Andererseits zeigte die Analyse der qPCR-Ergebnisse einen Unterschied zwischen den Studiengruppen (schlechtes, normales und hohes Ansprechen) in den Expressionsniveaus der Gene FSHR (rs6166), FSHR (rs6165), AMH, LHCGR und ESR1 (P ≤ 0,0001). Zusammenfassend zeigen die Ergebnisse dieser Studie, dass Polymorphismen in den Genen für die wichtige Fortpflanzungshormone (AMH, FSHR und ESR1) in Kombination mit den klinischen Merkmalen der Patientin und hormonellen Biomarkern verwendet werden könnten, um die ovarielle Reaktion auf Gonadotropen vorherzusagen, zu personalisieren und die Dosis von Gonadotropen vor Beginn des Stimulationsprotokolls anzupassen, um die Wirksamkeit zu verbessern und mögliche Komplikationen wie Zyklusabbruch und OHSS zu vermeiden; und schließlich zur Verbesserung der Schwangerschaftsrate bei Patientinnen, die sich einer ICSI-Behandlung unterziehen

Generic closed loop controller for power regulation in dual active bridge DC-DC converter with current stress minimization

This paper presents a comprehensive and generalized analysis of the bidirectional dual active bridge (DAB) DC/DC converter using triple phase shift (TPS) control to enable closed loop power regulation while minimizing current stress. The key new achievements are: a generic analysis in terms of possible conversion ratios/converter voltage gains (i.e. Buck/Boost/Unity), per unit based equations regardless of DAB ratings, and a new simple closed loop controller implementable in real time to meet desired power transfer regulation at minimum current stress. Per unit based analytical expressions are derived for converter AC RMS current as well as power transferred. An offline particle swarm optimization (PSO) method is used to obtain an extensive set of TPS ratios for minimizing the RMS current in the entire bidirectional power range of - 1 to 1 per unit. The extensive set of results achieved from PSO presents a generic data pool which is carefully analyzed to derive simple useful relations. Such relations enabled a generic closed loop controller design that can be implemented in real time avoiding the extensive computational capacity that iterative optimization techniques require. A detailed Simulink DAB switching model is used to validate precision of the proposed closed loop controller under various operating conditions. An experimental prototype also substantiates the results achieved

A programmable receiver front-end for multi-band/multi-standard applications

Nowadays, wireless communication devices need a compact wireless receiver, so that it can access all the available services at any time and at any location with minimum power consumption and compact area. The desire for covering all the service specifications tremendously increases the demand for multi-band/multi-standard wireless receivers. A reconfigurable receiver comes to give a hand. In this work, a universal programmable multi-band multi-standard receiver using CMOS technology is proposed. The receiver aims to target LTE specifications on the frequency range (700MHz-2.4GHz) as a case study to prove the concept of supporting multi-bands. The receiver is tested over three different frequencies 500MHz, 1GHz and 2GHz to prove its programmability. Sampling receivers and impedance translation technique are the main factors to approach the desired programmable receiver front-end. The receiver uses a quadrature band-pass charge sampling filter programmed via its controlling clocks. It forms the signal path which selects the signal, down-converts it to IF frequency and subsamples the signal decreasing the sampling frequency of the proceeding ADC. By adjusting the controlling clocks of the switches, the filter center frequency is maintained at the desired frequency. A time varying matching network based on impedance translation technique is used for multi-frequencies matching and further selectivity enhancing the receiver’s linearity. The receiver front-end architecture achieves a NF of (7: 9) dB, a gain of (23: 28) dB, an out-of-band IIP3 of (-1.9 : -5.5) dBm and an in-band IIP3 of (-1.9 : -5.7) dBm across the tested frequencies. The design is tested across process corners. The layout of the design occupies 0.45mm2. The design is tested post layout to prove its reliability

Modular multilevel converter with modified half-bridge submodule and arm filter for dc transmission systems with DC fault blocking capability

Although a modular multilevel converter (MMC) is universally accepted as a suitable converter topology for the high voltage dc transmission systems, its dc fault ride performance requires substantial improvement in order to be used in critical infrastructures such as transnational multi-terminal dc (MTDC) networks. Therefore, this paper proposes a modified submodule circuit for modular multilevel converter that offers an improved dc fault ride through performance with reduced semiconductor losses and enhanced control flexibility compared to that achievable with full-bridge submodules. The use of the proposed submodules allows MMC to retain its modularity; with semiconductor loss similar to that of the mixed submodules MMC, but higher than that of the half-bridge submodules. Besides dc fault blocking, the proposed submodule offers the possibility of controlling ac current in-feed during pole-to-pole dc short circuit fault, and this makes such submodule increasingly attractive and useful for continued operation of MTDC networks during dc faults. The aforesaid attributes are validated using simulations performed in MATLAB/SIMULINK, and substantiated experimentally using the proposed submodule topology on a 4-level small-scale MMC prototype

Genome-Wide Association Studies Of Depression And Alzheimer’s: Identifying Pleiotropic Snps

Background: Alzheimer’s disease (AD) has been rising globally, making it important to understand risk factors. Depression has been identified as a risk factor in epidemiological studies and meta-analysis. The possible mechanism could be pleiotropic. This study sought to identify the pleiotropic SNPs associated with depression and AD so that can understand the relationship between the two diseases more evident.Methods: The sample was from the UK biobank population, using the White European subpopulation. Depression was defined using self-reported depression status and electronic medical records. Proxy AD was defined by biological mother and father’s AD status. Two univariate association analysis was conducted using a linear mixed model in REGENIE. A p-value comparison was conducted for both analyses. Linkage disequilibrium (LD) clumping was conducted using a four MB region surrounding the index variant and an r2 threshold of 0.2. Results: The study had 406,7394 individuals in the depression sample and 409,704 individuals in the AD sample. The univariate analysis for depression identified five genome-wide significant SNPs, and the univariate analysis for AD identified 1,319 genome-wide significant SNPs. The p-value comparison found that 36 genome-wide significant SNPs for AD were genome-wide nominal for depression, with SNPs chromosome 8 having p-values for depression less than 0.001. The LD clumping identified a region in chromosome 19 associated with genes APOE and TOMM40. The leading SNP was rs2075650 and associated with the two phenotypes. Conclusion: The study found SNPs associated with depression and AD. Furthermore, SNP rs2075650 on chromosome 19 seems promising in understanding the relationship between depression and AD. Further studies must be conducted to understand the association that can lead to prevention methods

Modified dual active bridge DC/DC converter with improved efficiency and interoperability in hybrid LCC/VSC HVDC transmission grids

DC transmission grids are the promising electrical networks in the near future especially with the high penetration of large scale renewables. This paper proposes a modified version of the dual active bridge (DAB) DC/DC converter with AC link capacitors generating reactive power to compensate for non-active power consumption; hence mitigating current stresses and losses to improve efficiency. The proposed topology also enables the connectivity of current source line-commutated HVDC and voltage source HVDC technologies particularly during power reversal; a feature which conventional DAB is incapable of doing. Analysis and detailed design of the proposed converter are addressed and a comparative performance analysis is carried out with conventional DAB. Converter principle of operation is explained and Matlab/Simulink simulations are carried out to verify converter operation particularly under adverse conditions such as rated power reversal and DC fault conditions. A low scale prototype substantiates the theoretical analysis and simulation results

Efficient FPGA-Based Inference Architectures for Deep Learning Networks

L’apprentissage profond est devenu la technique de pointe pour de nombreuses applications de classification et de régression. Les modèles d’apprentissage profond, tels que les réseaux de neurones profonds (Deep Neural Network - DNN) et les réseaux de neurones convolutionnels (Convolutional Neural Network - CNN), déploient des dizaines de couches cachées avec des centaines de neurones pour obtenir une représentation significative des données d’entrée. La puissance des DNN et des CNN provient du fait qu’ils sont formés par apprentissage de caractéristiques extraites plutôt que par des algorithmes spécifiques à une tâche. Cependant, cela se fait aux dépens d’un coût de calcul élevé pour les processus d’apprentissage et d’inférence. Cela nécessite des accélérateurs avec de hautes performances et économes en énergie, en particulier pour les inférences lorsque le traitement en temps réel est important. Les FPGA offrent une plateforme attrayante pour accélérer l’inférence des DNN et des CNN en raison de leurs performances, dû à leur configurabilité et de leur efficacité énergétique. Dans cette thèse, nous abordons trois problèmes principaux. Premièrement, nous examinons le problème de la mise en oeuvre précise et efficace des DNN traditionnels entièrement connectés sur les FPGA. Bien que les réseaux de neurones binaires (Binary Neural Network - BNN) utilisent une représentation de données compacte sur un bit par rapport aux données à virgule fixe et à virgule flottante pour les DNN et les CNN traditionnels, ils peuvent encore nécessiter trop de ressources de calcul et de mémoire. Par conséquent, nous étudions le problème de l’implémentation des BNN sur FPGA en tant que deuxième problème. Enfin, nous nous concentrons sur l’introduction des FPGA en tant qu’accélérateurs matériels pour un plus grand nombre de développeurs de logiciels, en particulier ceux qui ne maîtrisent pas les connaissances en programmation sur FPGA. Pour résoudre le premier problème, et dans la mesure où l’implémentation efficace de fonctions d’activation non linéaires est essentielle à la mise en oeuvre de modèles d’apprentissage profond sur les FPGA, nous introduisons une implémentation de fonction d’activation non linéaire basée sur le filtre à interpolation de la transformée cosinus discrète (Discrete Cosine Transform Interpolation Filter - DCTIF). L’architecture d’interpolation proposée combine des opérations arithmétiques sur des échantillons stockés de la fonction de tangente hyperbolique et sur les données d’entrée. Cette solution offre une précision 3× supérieure à celle des travaux précédents, tout en utilisant une quantité similaire des ressources de calculs et une petite quantité de mémoire. Différentes combinaisons de paramètres du filtre DCTIF peuvent être choisies pour compenser la précision et la complexité globale du circuit de la fonction tangente hyperbolique.----------ABSTRACT: Deep learning has evolved to become the state-of-the-art technique for numerous classification and regression applications. Deep learning models, such as Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), deploy dozens of hidden layers with hundreds of neurons to learn a meaningful representation of the input data. The power of DNNs and CNNs comes from the fact that they are trained through feature learning rather than task-specific algorithms. However, this comes at the expense of high computational cost for both training and inference processes. This necessitates high-performance and energyefficient accelerators, especially for inference where real-time processing matters. FPGAs offer an appealing platform for accelerating the inference of DNNs and CNNs due to their performance, configurability and energy-efficiency. In this thesis, we address three main problems. Firstly, we consider the problem of realizing a precise but efficient implementation of traditional fully connected DNNs in FPGAs. Although Binary Neural Networks (BNNs) use compact data representation (1-bit) compared to fixedpoint data and floating-point representation in traditional DNNs and CNNs, they may still need too many computational and memory resources. Therefore, we study the problem of implementing BNNs in FPGAs as the second problem. Finally, we focus on introducing FPGAs as accelerators to a wider range of software developers, especially those who do not posses FPGA programming knowledge. To address the first problem, and since efficient implementation of non-linear activation functions is essential to the implementation of deep learning models on FPGAs, we introduce a non-linear activation function implementation based on the Discrete Cosine Transform Interpolation Filter (DCTIF). The proposed interpolation architecture combines arithmetic operations on the stored samples of the hyperbolic tangent function and on input data. It achieves almost 3× better precision than previous works while using a similar amount of computational resources and a small amount of memory. Various combinations of DCTIF parameters can be chosen to trade off the accuracy and the overall circuit complexity of the tanh function. In an attempt to address the first and third problems, we introduce a Single hidden layer Neural Network (SNN) multiplication-free overlay architecture with fully connected DNN-level performance. This FPGA inference overlay can be used for applications that are normally solved with fully connected DNNs. The overlay avoids the time needed to synthesize, place, route and regenerate a new bitstream when the application changes. The SNN overlay in puts and activations are quantized to power-of-two values, which allows utilizing shift units instead of multipliers. Since the overlay is a SNN, we fill the FPGA chip with the maximum possible number of neurons that can work in parallel in the hidden layer. We evaluate the proposed architecture on typical benchmark datasets and demonstrate higher throughput with respect to the state-of-the-art while achieving the same accuracy. In addition, the SNN overlay makes the power and versatility of FPGAs available to a wider DNN user community and to improve DNN design efficiency

Enhanced performance modified discontinuous PWM technique for three phase Z-source inverter

Various industrial applications require a voltage conversion stage from DC to AC. Among them, commercial renewable energy systems (RES) need a voltage buck and/or boost stage for islanded/grid connected operation. Despite the excellent performance offered by conventional two-stage converter systems (DC-DC followed by dc-ac stages), the need for a single-stage conversion stage is attracting more interest for cost and size reduction reasons. Although voltage source inverters (VSIs) are voltage buck-only converters, single stage current source inverters (CSIs) can offer voltage boost features, although at the penalty of using a large DC-link inductor. Boost inverters are a good candidate with the demerit of complicated control strategies. The impedance source (Z-source) inverter is a high-performance competitor as it offers voltage buck/boost in addition to a reduced passive component size. Several pulse width modulation (PWM) techniques have been presented in the literature for three-phase Z-source inverters. Various common drawbacks are annotated, especially the non-linear behavior at low modulation indices and the famous trade-off between the operating range and the converter switches' voltage stress. In this paper, a modified discontinuous PWM technique is proposed for a three-phase z-source inverter offering: (i) smooth voltage gain variation, (ii) a wide operating range, (iii) reduced voltage stress, and (iv) improved total harmonic distortion (THD). Simulation, in addition to experimental results at various operating conditions, validated the proposed PWM technique's superior performance compared to the conventional PWM techniques