Extracellular Matrix Protein Tenascin C Increases Phagocytosis Mediated by CD47 Loss of Function in Glioblastoma.

Glioblastomas (GBM) are highly infiltrated by myeloid-derived innate immune cells that contribute to the immunosuppressive nature of the brain tumor microenvironment (TME). CD47 has been shown to mediate immune evasion, as the CD47-SIRPα axis prevents phagocytosis of tumor cells by macrophages and other myeloid cells. In this study, we established CD47 homozygous deletion (CD47-/-) in human and mouse GBM cells and investigated the impact of eliminating the "don't eat me" signal on tumor growth and tumor-TME interactions. CD47 knockout (KO) did not significantly alter tumor cell proliferation in vitro but significantly increased phagocytosis of tumor cells by macrophages in cocultures. Compared with CD47 wild-type xenografts, orthotopic xenografts derived from CD47-/- tumor cells grew significantly slower with enhanced tumor cell phagocytosis and increased recruitment of M2-like tumor-associated microglia/macrophages (TAM). CD47 KO increased tumor-associated extracellular matrix protein tenascin C (TNC) in xenografts, which was further examined in vitro. CD47 loss of function upregulated TNC expression in tumor cells via a Notch pathway-mediated mechanism. Depletion of TNC in tumor cells enhanced the growth of CD47-/- xenografts in vivo and decreased the number of TAM. TNC knockdown also inhibited phagocytosis of CD47-/- tumor cells in cocultures. Furthermore, TNC stimulated release of proinflammatory factors including TNFα via a Toll-like receptor 4 and STAT3-dependent mechanism in human macrophage cells. These results reveal a vital role for TNC in immunomodulation in brain tumor biology and demonstrate the prominence of the TME extracellular matrix in affecting the antitumor function of brain innate immune cells. SIGNIFICANCE: These findings link TNC to CD47-driven phagocytosis and demonstrate that TNC affects the antitumor function of brain TAM, facilitating the development of novel innate immune system-based therapies for brain tumors

Micromachined coupled resonator butler matrix

This thesis presents the design and characterisation of a micromachined Butler Matrix operating at WR-3 band (220 GHz to 325 GHz). For this thesis a two port Butler matrix is used, here there are two input ports and two output ports. When signal is applied to one of the input ports then it is equally split between the output ports, but has a 90 degree phase shift. In general Butler matrices can have many ports. Conventionally transmission lines are used to make a Butler Matrix but in this thesis, the Butler Matrix is realised only in resonators. This technique is implemented by the appropriate couplings between resonators and the coupling between the resonators and the external ports. The method has the advantage of applying to any type of resonator can be used no matter what its physical structure. SU-8 photoresist micromachining technology is used for fabrication and three metal-coated SU-8 layers are used to implement this micromachined coupled resonator Butler Matrix. The fabrication process was not successful thus no useful measurement results had been made. Low loss bends are designed for the connections between the Butler Matrix and the loads and flanges. A two-element slotted waveguide antenna array is designed to be connected to the Butler Matrix to build a beamforming system and to verify the characterisation of the Butler Matrix

IMPACT OF OUTCOME MEASUREMENTS AND CUTOFF SCORES ON IDENTIFYING PREDICTORS IN TREATMENT RESPONSE IN AN APATHY IN DEMENTIA TRIAL

Objective: The Apathy in Dementia Methylphenidate Trial 2 (ADMET 2) showed the effect of methylphenidate in treating apathy in Alzheimer’s disease with heterogeneous response, using the Neuropsychiatric Inventory Apathy Subscale (NPI) measurement. I assessed the impact on identification for clinical response predictors of different outcome measurements and different cutoff scores for those measurements, including the Clinical Global Impression of Change (CGIC), the Dementia Apathy Interview and Rating (DAIR), and the Neuropsychiatric Inventory (NPI). Design and Setting: The Apathy in Dementia Methylphenidate Trial 2 (ADMET 2) was a Phase III, randomized, placebo-controlled multi-center trial. Our work used univariate and multivariate analyses to pool from 23 clinical potential predictors of response to methylphenidate. Participants: 99 patients assigned to the active treatment arm in the 200-patient ADMET 2 trial, conducted at 10 sites in the U.S. and Canada. Methods: The primary outcome measurements included the Neuropsychiatric Inventory Apathy Subscale (NPI apathy), the Clinical Global Impression of Change (CGIC) and the Dementia Apathy Interview and Rating (DAIR), each with 3 different cut-off scores. A two-step process that included a univariate and a multivariate logistic regression was used to determine predictors of response to methylphenidate. Results: In the methylphenidate treatment group, 99 participants (33 females and 65 males) completed the 6-month follow-up. After developing prediction models utilizing three distinct measures of apathy, the results showed very different groups of clinical predictors. Examination of specific cutoffs for each of the outcome measures showed that the models were stable in NPI and DAIR models but not in CGIC model. The consistent predictors across all models were robust indicators, including age, current smoking status, and a history of major depression. The predictor of clinical response associated with current trazodone use was also a good indicator in the majority of models. Conclusion: The selection of outcome measures and the determination of cutoff scores influence the identification of predictors. Different outcome measures and cutoff scores had a significant impact on the univariate prediction models, altering the selection of potential predictors that qualified for inclusion in the multivariate models. Age, smoking status and major depression showed significant improvement among all NPI-A, CGIC and DAIR measures with methylphenidate. When assessing the ROC curve and AUC values, it became evident that the DAIR model outperformed the NPI and CGIC models, as it exhibited a higher AUC, signifying superior performance

IMPACT OF OUTCOME MEASUREMENTS AND CUTOFF SCORES ON IDENTIFYING PREDICTORS IN TREATMENT RESPONSE IN AN APATHY IN DEMENTIA TRIAL

Objective: The Apathy in Dementia Methylphenidate Trial 2 (ADMET 2) showed the effect of methylphenidate in treating apathy in Alzheimer’s disease with heterogeneous response, using the Neuropsychiatric Inventory Apathy Subscale (NPI) measurement. I assessed the impact on identification for clinical response predictors of different outcome measurements and different cutoff scores for those measurements, including the Clinical Global Impression of Change (CGIC), the Dementia Apathy Interview and Rating (DAIR), and the Neuropsychiatric Inventory (NPI). Design and Setting: The Apathy in Dementia Methylphenidate Trial 2 (ADMET 2) was a Phase III, randomized, placebo-controlled multi-center trial. Our work used univariate and multivariate analyses to pool from 23 clinical potential predictors of response to methylphenidate. Participants: 99 patients assigned to the active treatment arm in the 200-patient ADMET 2 trial, conducted at 10 sites in the U.S. and Canada. Methods: The primary outcome measurements included the Neuropsychiatric Inventory Apathy Subscale (NPI apathy), the Clinical Global Impression of Change (CGIC) and the Dementia Apathy Interview and Rating (DAIR), each with 3 different cut-off scores. A two-step process that included a univariate and a multivariate logistic regression was used to determine predictors of response to methylphenidate. Results: In the methylphenidate treatment group, 99 participants (33 females and 65 males) completed the 6-month follow-up. After developing prediction models utilizing three distinct measures of apathy, the results showed very different groups of clinical predictors. Examination of specific cutoffs for each of the outcome measures showed that the models were stable in NPI and DAIR models but not in CGIC model. The consistent predictors across all models were robust indicators, including age, current smoking status, and a history of major depression. The predictor of clinical response associated with current trazodone use was also a good indicator in the majority of models. Conclusion: The selection of outcome measures and the determination of cutoff scores influence the identification of predictors. Different outcome measures and cutoff scores had a significant impact on the univariate prediction models, altering the selection of potential predictors that qualified for inclusion in the multivariate models. Age, smoking status and major depression showed significant improvement among all NPI-A, CGIC and DAIR measures with methylphenidate. When assessing the ROC curve and AUC values, it became evident that the DAIR model outperformed the NPI and CGIC models, as it exhibited a higher AUC, signifying superior performance

A Compressible Reconfigurable Frequency Selective Surface

Frequency selective surfaces (FSSs) are periodic structures which perform a spatial filtering operation, such as band-pass, band-stop, etc. in application of multiband reflector antennas, radomes, absorbers and so on. The filtering is normally achieved by periodic arrangement of metallic shapes or slots on a dielectric slab. The responses of the FSS are impacted by the unit cell structure, type of the element, material properties and arrangement of the array. The interest in frequency agile and wideband systems has led to an expanding design space to include reconfigurable FSSs. Reconfigurable FSSs have the advantages of wider range of operating frequencies, compensation for fabrication errors by tuning mechanically. The reconfiguration could be achieved by solid-state devices (varactors, PIN diodes), usage of semiconductors, changing the substrates’ characteristics or the dimensions of the elements. Tunable FSS are in practical need for applications such as tunable radomes and adaptive screening of unwanted wireless transmissions. For a continuous tuning over the frequency range, fluidic tuning and mechanical reconfigurable FSSs claim to deliver the most dramatic continuous parameter variations. In this work, a 3-D mechanically tunable FSS has been presented with a band-stop characteristic. The 3-D FSSs has displayed greater flexibility and controllability compared to conventional 2D FSSs. They have the ability of setting the resonant frequency and continuous tuning the operational filter states with a change in length or height of the resonator, offering the tuning and switching functionality from the same structure without the use of additional electrical components. In this design, the reconfiguration is achieved by compressing the FSS structure, varying the height and the element shapes. Simulations and measurements of a FSS prototype manufactured by 3-D printer, some metallic and dielectric components are provided to demonstrate the tuning capability in S-band. A 490 MHz tuning range could be achieved by this design, while adding some dielectric material inside, a wider tuning range of 600 MHz could be reached. The measurement results show a good match with the design by HFSS