Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

Background

The Triple-Negative Breast Cancer (TNBC) molecular subtyping and target identification based on Immunohistochemistry (IHC) is of considerable worth for routine use. Yet, literature on this topic is limited worldwide and needs to be enriched with data from different populations.

Methods

We assessed the IHC expression of subtyping biomarkers (Cytokeratins 5, 14 and 17, Epidermal Growth Factor Receptor, Claudins 3 and 7, E-cadherin, Vimentin and Androgen receptor) and predictive biomarkers (Tumor-infiltrating lymphocytes (TILs) density, Breast Cancer Antigen 1 (BRCA1) and P53) in a cohort of TNBC patients. Clinicopathologic parameters and overall survival (OS) were investigated as well.

Results

The patients were aged 50.11 ± 12.13y (more than 40y in 76.56% of patients), and 23.44% had a BC family history. They were in a non-advanced stage: 51.6% T2 stage, 56.2% negative lymph node involvement, 76.6% without metastasis and 64.1% grade II Scarff-Bloom-Richardson classification (SBR).

The IHC subtypes were: 53.1% Basal-like1 (BL1), 6.3% Basal-like2 (BL2), 17.2% Mesenchymal (MES), 9.4% Luminal Androgen Receptor (LAR), 4.7% Mixed subtype and 9.4% “Unclassified” type. The LAR subtype involved the youngest patients (40.17 ± 8.68y, p = 0.02). The “Unclassified” subtype expressed the p53 mutated-type pattern more frequently (100%, p = 0.07). The BRCA1 mutated pattern and TILs infiltration were present in (23.44% and 37.5% of patients, respectively).

The OS of the subtypes differed significantly (p = 0.007, log-rank test). The subtypes median OS were, respectively, 15.47 mo. (Unclassified), 18.94 mo. (BL2), 27.23 mo. (MES), 27.28 mo. (Mixed), 30.88 mo. (BL1), and 45.07 mo. (LAR). There was no difference in the OS following age, BRCA1 expression, p53 pattern and TILs density. Though, the OS following the TNM stage was different (p = 0.001). A multivariable Cox proportional hazards regression analysis showed that TNM staging and TNBC subtypes, independently influence the OS (p < 0.001 and p = 0.017, respectively).

Hence, IHC is useful in TNBC subtyping for prognostic purposes and in the identification of therapeutic biomarkers. Further investigation is required to confirm our results and to implement IHC as a routine tool to improve patient’s care.

This study set out to examine the thermo-mechanical behavior of gypsum-protected steel columns (GPSC) exposed to fire, including the cooling phase, through numerical analyses with the aim of better understanding the effect of protection materials and identifying the possibility of delayed failure of GPSC during this critical period. A parametric study has been performed with the SAFIR program using a sequentially decoupled thermal structural analysis. The examined factors are the shape of the columns, the fire intensity, and the thickness of the protection. Gypsum serves as insulation, providing passive protection to prevent the degradation of steel mechanical properties and to mitigate and delay the collapse of steel columns during fire exposure. Different thicknesses of gypsum were considered (1 mm, 3 mm, and 5 mm) in order to analyze the effect of the rate of heat storage on the delayed collapse during and after fire exposure. The simulations were performed considering ISO fire and parametric temperature–time curves, which include a cooling regime that is linear. The findings show that the failure of the GPSC over the period of cooling is a possible event where the protection acts as a cooling retarder, which leads to a delayed collapse. Columns with massive sections and thick layers of protection are the most susceptible to delayed failure. Overall, this paper provides a real assessment of the load capacity in a natural fire situation, and the results highlight the possibility of delayed collapse of GPSC.

This study set out to examine the thermo-mechanical behavior of gypsum-protected steel columns (GPSC) exposed to fire, including the cooling phase, through numerical analyses with the aim of better understanding the effect of protection materials and identifying the possibility of delayed failure of GPSC during this critical period. A parametric study has been performed with the SAFIR program using a sequentially decoupled thermal structural analysis. The examined factors are the shape of the columns, the fire intensity, and the thickness of the protection. Gypsum serves as insulation, providing passive protection to prevent the degradation of steel mechanical properties and to mitigate and delay the collapse of steel columns during fire exposure. Different thicknesses of gypsum were considered (1 mm, 3 mm, and 5 mm) in order to analyze the effect of the rate of heat storage on the delayed collapse during and after fire exposure. The simulations were performed considering ISO fire and parametric temperature–time curves, which include a cooling regime that is linear. The findings show that the failure of the GPSC over the period of cooling is a possible event where the protection acts as a cooling retarder, which leads to a delayed collapse. Columns with massive sections and thick layers of protection are the most susceptible to delayed failure. Overall, this paper provides a real assessment of the load capacity in a natural fire situation, and the results highlight the possibility of delayed collapse of GPSC.

This study set out to examine the thermo-mechanical behavior of gypsum-protected steel columns (GPSC) exposed to fire, including the cooling phase, through numerical analyses with the aim of better understanding the effect of protection materials and identifying the possibility of delayed failure of GPSC during this critical period. A parametric study has been performed with the SAFIR program using a sequentially decoupled thermal structural analysis. The examined factors are the shape of the columns, the fire intensity, and the thickness of the protection. Gypsum serves as insulation, providing passive protection to prevent the degradation of steel mechanical properties and to mitigate and delay the collapse of steel columns during fire exposure. Different thicknesses of gypsum were considered (1 mm, 3 mm, and 5 mm) in order to analyze the effect of the rate of heat storage on the delayed collapse during and after fire exposure. The simulations were performed considering ISO fire and parametric temperature–time curves, which include a cooling regime that is linear. The findings show that the failure of the GPSC over the period of cooling is a possible event where the protection acts as a cooling retarder, which leads to a delayed collapse. Columns with massive sections and thick layers of protection are the most susceptible to delayed failure. Overall, this paper provides a real assessment of the load capacity in a natural fire situation, and the results highlight the possibility of delayed collapse of GPSC.

The article presents a hybrid controller based on the Incremental Conductance (Inc-Cond) and Interval Type-2 Fuzzy Logic (IT-2FL) algorithms as a Maximum Power Point Tracker (MPPT). The controller employs a three-phase Interleaved Boost Converter (IBC), which operates based on the pulses generated by the MPPT to ensure that the photovoltaic (PV) system operates at or near its Maximum Power Point (MPP). IT-2FL enhances the tracking process by applying rule fuzzification and managing uncertainties in response to significant fluctuations in climatic conditions. The proposed controller demonstrates precise and rapid convergence to the MPP, outperforming the individual application of both component methods, as well as traditional fuzzy logic, even when combined with Inc-Cond. The fault tolerance of the proposed tracker is validated through MATLAB simulations under various operational scenarios, evaluating response time, MPP tracking accuracy, efficiency, and other parameters.

The article presents a hybrid controller based on the Incremental Conductance (Inc-Cond) and Interval Type-2 Fuzzy Logic (IT-2FL) algorithms as a Maximum Power Point Tracker (MPPT). The controller employs a three-phase Interleaved Boost Converter (IBC), which operates based on the pulses generated by the MPPT to ensure that the photovoltaic (PV) system operates at or near its Maximum Power Point (MPP). IT-2FL enhances the tracking process by applying rule fuzzification and managing uncertainties in response to significant fluctuations in climatic conditions. The proposed controller demonstrates precise and rapid convergence to the MPP, outperforming the individual application of both component methods, as well as traditional fuzzy logic, even when combined with Inc-Cond. The fault tolerance of the proposed tracker is validated through MATLAB simulations under various operational scenarios, evaluating response time, MPP tracking accuracy, efficiency, and other parameters.

The article presents a hybrid controller based on the Incremental Conductance (Inc-Cond) and Interval Type-2 Fuzzy Logic (IT-2FL) algorithms as a Maximum Power Point Tracker (MPPT). The controller employs a three-phase Interleaved Boost Converter (IBC), which operates based on the pulses generated by the MPPT to ensure that the photovoltaic (PV) system operates at or near its Maximum Power Point (MPP). IT-2FL enhances the tracking process by applying rule fuzzification and managing uncertainties in response to significant fluctuations in climatic conditions. The proposed controller demonstrates precise and rapid convergence to the MPP, outperforming the individual application of both component methods, as well as traditional fuzzy logic, even when combined with Inc-Cond. The fault tolerance of the proposed tracker is validated through MATLAB simulations under various operational scenarios, evaluating response time, MPP tracking accuracy, efficiency, and other parameters.

The article presents a hybrid controller based on the Incremental Conductance (Inc-Cond) and Interval Type-2 Fuzzy Logic (IT-2FL) algorithms as a Maximum Power Point Tracker (MPPT). The controller employs a three-phase Interleaved Boost Converter (IBC), which operates based on the pulses generated by the MPPT to ensure that the photovoltaic (PV) system operates at or near its Maximum Power Point (MPP). IT-2FL enhances the tracking process by applying rule fuzzification and managing uncertainties in response to significant fluctuations in climatic conditions. The proposed controller demonstrates precise and rapid convergence to the MPP, outperforming the individual application of both component methods, as well as traditional fuzzy logic, even when combined with Inc-Cond. The fault tolerance of the proposed tracker is validated through MATLAB simulations under various operational scenarios, evaluating response time, MPP tracking accuracy, efficiency, and other parameters.

This study explores a novel approach to regulate blood glucose levels in individuals with type I diabetes, employing the widely used model predictive control (MPC) strategy in type 1 diabetes mellitus therapy and clinical trials. The MPC algorithm is implemented based on Magdelaine’s long-term glucose-insulin model, which encompasses real-life characteristics often absent in other prevalent models. The control strategy is evaluated through simulations involving 10 virtual patients from existing literature. The simulations encompass fasting scenarios and a closed-loop control scenario involving three meals. MPC results are compared to those of the “optimal” conventional insulin daily injections therapy (open-loop treatment), especially under “aggressive conditions” including elevated initial blood glucose levels, substantial carbohydrate intake, closely spaced meal times, and incorporating a time delay between plasma glucose concentration and its subcutaneous measurement. The MPC algorithm demonstrated remarkable efficacy in glycemic control for 80% of patients, achieving an average time-in-range percentage exceeding 80% with no hypoglycemic episodes. This aligns with the American Diabetes Association’s recommendation of spending at least 70% of the time in the target range for effective glycemic control and maintaining an average time spent in hypoglycemia of less than 4%. However, the same MPC controller exhibited suboptimal performance for two patients, with an average time spent in hypoglycemia exceeding 8%. These findings underscore the need for individualized adjustments of MPC parameters or alternative control strategies to optimize glycemic management in all patients.

This study explores a novel approach to regulate blood glucose levels in individuals with type I diabetes, employing the widely used model predictive control (MPC) strategy in type 1 diabetes mellitus therapy and clinical trials. The MPC algorithm is implemented based on Magdelaine’s long-term glucose-insulin model, which encompasses real-life characteristics often absent in other prevalent models. The control strategy is evaluated through simulations involving 10 virtual patients from existing literature. The simulations encompass fasting scenarios and a closed-loop control scenario involving three meals. MPC results are compared to those of the “optimal” conventional insulin daily injections therapy (open-loop treatment), especially under “aggressive conditions” including elevated initial blood glucose levels, substantial carbohydrate intake, closely spaced meal times, and incorporating a time delay between plasma glucose concentration and its subcutaneous measurement. The MPC algorithm demonstrated remarkable efficacy in glycemic control for 80% of patients, achieving an average time-in-range percentage exceeding 80% with no hypoglycemic episodes. This aligns with the American Diabetes Association’s recommendation of spending at least 70% of the time in the target range for effective glycemic control and maintaining an average time spent in hypoglycemia of less than 4%. However, the same MPC controller exhibited suboptimal performance for two patients, with an average time spent in hypoglycemia exceeding 8%. These findings underscore the need for individualized adjustments of MPC parameters or alternative control strategies to optimize glycemic management in all patients.

This paper presents a novel strain-based finite element (NSBPE4K) developed for the free vibration analysis of thin plates, both with and without cutouts. The element incorporates three primary degrees-of-freedom per node: a transverse displacement (w) and two normal rotations (θx, θy) about the x and y axes, respectively. The displacement field is formulated based on assumed functions for the strain components, ensuring the compatibility equations are satisfied. The non-conforming element was successfully implemented in the ABAQUS software using the UEL subroutine (user element). Free vibration analysis results demonstrate the exceptional efficiency and accuracy of the new element. The results obtained with the present element excel those obtained with standard ABAQUS elements and other non-conforming elements found in the literature. This superiority is noticeable in free vibration scenarios, demonstrating the effectiveness of the proposed finite element for accurate and reliable simulation of the vibrational behavior of thin plates.

This paper presents a novel strain-based finite element (NSBPE4K) developed for the free vibration analysis of thin plates, both with and without cutouts. The element incorporates three primary degrees-of-freedom per node: a transverse displacement (w) and two normal rotations (θx, θy) about the x and y axes, respectively. The displacement field is formulated based on assumed functions for the strain components, ensuring the compatibility equations are satisfied. The non-conforming element was successfully implemented in the ABAQUS software using the UEL subroutine (user element). Free vibration analysis results demonstrate the exceptional efficiency and accuracy of the new element. The results obtained with the present element excel those obtained with standard ABAQUS elements and other non-conforming elements found in the literature. This superiority is noticeable in free vibration scenarios, demonstrating the effectiveness of the proposed finite element for accurate and reliable simulation of the vibrational behavior of thin plates.

This paper presents a novel strain-based finite element (NSBPE4K) developed for the free vibration analysis of thin plates, both with and without cutouts. The element incorporates three primary degrees-of-freedom per node: a transverse displacement (w) and two normal rotations (θx, θy) about the x and y axes, respectively. The displacement field is formulated based on assumed functions for the strain components, ensuring the compatibility equations are satisfied. The non-conforming element was successfully implemented in the ABAQUS software using the UEL subroutine (user element). Free vibration analysis results demonstrate the exceptional efficiency and accuracy of the new element. The results obtained with the present element excel those obtained with standard ABAQUS elements and other non-conforming elements found in the literature. This superiority is noticeable in free vibration scenarios, demonstrating the effectiveness of the proposed finite element for accurate and reliable simulation of the vibrational behavior of thin plates.

Conductors serve as essential components in various electrical and electronic applications (steel, aircraft, and nuclear industries). Therefore, an accurate evaluation of their electrical parameters, in particular their electrical conductivity (σ), remains critical for assessing their performance in industrial processes. Although numerous eddy current based methods exist for conductivity measurement, this study approaches the problem through inverse problem solving. A novel approach integrating Eddy Current Testing (ECT) with Artificial Neural Networks (ANNs) is proposed to determine electrical conductivity from probe impedance measurements. An experimental setup has been developed that includes a custom-designed bobbin coil probe used in conjunction with metal plate samples (targets) and data acquisition and signal processing systems. To validate the introduced approach, conductivity values predicted by the ANN model were rigorously compared with reference measurements obtained using the four-point Direct Current Potential Drop (DCPD) technique. This comparative analysis demonstrates the robustness and measurement fidelity of the proposed approach.

Conductors serve as essential components in various electrical and electronic applications (steel, aircraft, and nuclear industries). Therefore, an accurate evaluation of their electrical parameters, in particular their electrical conductivity (σ), remains critical for assessing their performance in industrial processes. Although numerous eddy current based methods exist for conductivity measurement, this study approaches the problem through inverse problem solving. A novel approach integrating Eddy Current Testing (ECT) with Artificial Neural Networks (ANNs) is proposed to determine electrical conductivity from probe impedance measurements. An experimental setup has been developed that includes a custom-designed bobbin coil probe used in conjunction with metal plate samples (targets) and data acquisition and signal processing systems. To validate the introduced approach, conductivity values predicted by the ANN model were rigorously compared with reference measurements obtained using the four-point Direct Current Potential Drop (DCPD) technique. This comparative analysis demonstrates the robustness and measurement fidelity of the proposed approach.

Conductors serve as essential components in various electrical and electronic applications (steel, aircraft, and nuclear industries). Therefore, an accurate evaluation of their electrical parameters, in particular their electrical conductivity (σ), remains critical for assessing their performance in industrial processes. Although numerous eddy current based methods exist for conductivity measurement, this study approaches the problem through inverse problem solving. A novel approach integrating Eddy Current Testing (ECT) with Artificial Neural Networks (ANNs) is proposed to determine electrical conductivity from probe impedance measurements. An experimental setup has been developed that includes a custom-designed bobbin coil probe used in conjunction with metal plate samples (targets) and data acquisition and signal processing systems. To validate the introduced approach, conductivity values predicted by the ANN model were rigorously compared with reference measurements obtained using the four-point Direct Current Potential Drop (DCPD) technique. This comparative analysis demonstrates the robustness and measurement fidelity of the proposed approach.

Conductors serve as essential components in various electrical and electronic applications (steel, aircraft, and nuclear industries). Therefore, an accurate evaluation of their electrical parameters, in particular their electrical conductivity (σ), remains critical for assessing their performance in industrial processes. Although numerous eddy current based methods exist for conductivity measurement, this study approaches the problem through inverse problem solving. A novel approach integrating Eddy Current Testing (ECT) with Artificial Neural Networks (ANNs) is proposed to determine electrical conductivity from probe impedance measurements. An experimental setup has been developed that includes a custom-designed bobbin coil probe used in conjunction with metal plate samples (targets) and data acquisition and signal processing systems. To validate the introduced approach, conductivity values predicted by the ANN model were rigorously compared with reference measurements obtained using the four-point Direct Current Potential Drop (DCPD) technique. This comparative analysis demonstrates the robustness and measurement fidelity of the proposed approach.