Renal tubule has distinct metabolic features and functional activity that may be altered during kidney disease. In this paper, we present label-free functional activity imaging of renal tubule in normal and obstructed mouse kidney models using three-dimensional (3D) dynamic optical coherence tomography (OCT) ex vivo. To create an obstructed kidney model, we ligated the ureter of the left kidney for either 7 or 14 days. Two different dynamic OCT (DOCT) methods were implemented to access the slow and fast activity of the renal tubules: a logarithmic intensity variance (LIV) method and a complex-correlation-based method. Three-dimensional DOCT data were acquired with a 1.3  The renal tubular compartment consists of a proximal convoluted tubule (PCT), an intermediate tubule (loop of Henle), and a distal convoluted tubule (DCT). The renal tubules (mainly PCT and DCT) are responsible for metabolic needs such as fatty acid oxidation, glucose metabolism, and amino acid metabolism6.The kidney is a complex organ that performs several vital tasks including filtering the blood, removing toxins, and maintaining an acid-base balance. The functional unit of the kidney is the nephron, which is composed of renal corpuscles (glomerulus with Bowman’s capsule) and tubular segments8. The renal tubules are vulnerable to both acute and chronic kidney injuries, and it is known that during such injuries, renal tubular metabolism is altered or even damaged9. To understand the metabolism of healthy renal tubules and how it changes during kidney injury, it is necessary to investigate the functional activity of renal tubules using small animal models. As the renal tubule extends from the renal cortex to the deep region and has a three-dimensional architecture, it is important to visualize the functional activity of deep renal tissue without any slicing.The kidney can be acutely injured by ureter obstruction, ischemia, toxins, drugs, and infection, or can be chronically damaged by diabetes mellitus or hypertension12. Hence, it is the preferred experimental tool for investigating kidney physiology and diseases. TPM uses fluorescent probes to visualize structural and functional changes in the kidney. Due to the use of fluorescent probes, the acquired data of TPM are compromised by photobleaching and phototoxicity. Additionally, TPM has a limited imaging penetration depth of around 150 12. This limited penetration depth poses a challenge for visualizing the functional activity of deep renal tubule compartments. Therefore, an imaging technique that can accurately assess both the structural and functional activity of renal tubules without the use of exogenous contrast agents is required.Two-photon microscopy (TPM) allows researchers to visualize and understand dynamic intracellular and intercellular activities in kidney tissue13. OCT has resolution down to a few micrometers and imaging penetration of a few millimeters (around 1–2 mm). Several studies demonstrated the ability of OCT imaging to visualize three-dimensional internal microstructures of the kidney, such as renal tubules and glomerulus either ex vivo or in vivo15. Although OCT is particularly suitable for examining renal tissue, it can only visualize its static property, such as renal morphology. The functional activity of renal tubule compartments cannot be accessed using conventional OCT.Optical coherence tomography (OCT) is a label-free, cross-sectional, and three-dimensional imaging modality that uses low-coherence interferometry principles to reconstruct tomographic images of tissue structures22. This is achieved by acquiring several OCT frames with millisecond temporal resolution over a period of several seconds, followed by statistical analysis of the time-sequence signal. It was first introduced using a time-domain full-field OCT which generates en face dynamics image16 and was later implemented in Fourier-domain OCT that generates cross-sectional (B-scans) dynamics image19. Three-dimensional visualization of dynamics imaging has also become possible using ultra-fast OCT systems20 or long measurement time24. Recently, we demonstrated a custom-designed scanning protocol that uses a standard-speed swept-source OCT system to acquire volumetric tomography of tissue dynamics within a few seconds25.This limitation of conventional OCT, i.e., functional activity investigation can be overcome using extended OCT contrast so-called dynamic OCT (DOCT). Using DOCT, it is possible to visualize functional tissue structures in living organisms at a cellular or sub-cellular scale without the need for contrast agents26, frequency19, and time-correlation analysis28 of OCT signal fluctuation. Among the magnitude methods, logarithmic intensity variance (LIV) contrasts the fluctuation magnitude of the OCT signal intensity18. In our previous studies, LIV successfully revealed high-contrast dynamic structures in ex vivo mouse organs30 and in vitro tumor spheroids25. Among the time-correlation methods, Lee et al. used complex correlation analysis of time-sequential OCT signals to extract the velocity and diffusion coefficient of moving particles27. In general, the complex correlation-based algorithm is suitable for visualizing fast  tissue activity, whereas LIV is sensitive to relatively slow  functional activity. Hence, a combination of LIV and complex correlation-based methods may provide a more comprehensive understanding of renal tubule dynamics.Several DOCT methods for visualizing the functional dynamic structures of tissue have recently been demonstrated. These methods can be categorized into magnitude25 within a 6.12-s time window with an inter-frame time interval of 409.6 ms. For the fast activity visualization, complex correlation of time sequence OCT frames31 was computed within a 39-ms time window with an interframe time interval of 12.8 ms. Here, we refer to the correlation-based dynamic OCT contrast as ‘fast dynamic OCT (Fast-DOCT)’. DOCT imaging was performed using our custom-made standard-speed  swept-source OCT system33. Two different kidney model tissues were studied, involving normal and obstructed kidney models. For the obstructed kidney model, the ureter of the left kidney was obstructed for either 7 or 14 days. LIV visualizes differences in the functional activity of morphological renal tubules between normal and obstructed kidney models.In this paper, we demonstrate three-dimensional renal tubular functional activity imaging using DOCT. Two different signal processing algorithms and two custom-designed scanning protocols were used to access the slow and fast activity of renal tubule. For the slow functional activity imaging, the LIV of time-sequence OCT frames was calculated33. To capture the dynamic speckle patterns in live renal tissues, we recorded 16 frames at a single location within the sample, with an interframe time interval of 409.6 ms over a time window of 6.14 s. The LIV image is reconstructed by calculating the magnitude of the speckle fluctuation, which is determined by analyzing the temporal variance of the time-sequence logarithmic (dB) scale OCT intensity signal within a 6.14-s time window (see the “18 for detailed derivation). Fast-DOCT was obtained by complex correlation analysis31 among four repeated frames acquired with an inter-frame time separation of 13 ms within a time window of 39 ms.We used swept-source OCT microscopy to capture the dynamics in renal tissues. The OCT microscope used was reported previously “” sectionFigure mentclass2pt{minim30. In the LIV en face slice, small structural formations (yellow arrows) and some spatially isolated high-LIV spots (white arrowheads) are visible  spots, and no tail artifacts are observable Fig. e. Simila34. In addition to the glomeruli, renal tissue is comprised of convoluted renal tubule structures, which can be identified in the histology  for histology. The histology image Fig. g correspmentclass2pt{minimmentclass2pt{minimmentclass2pt{minimThe reconstructed slab average projection images from the three-dimensional data of the dB-scale OCT intensity, LIV, and Fast-DOCT are displayed in Fig. mentclass2pt{minimTo confirm whether the dynamics imaging method can detect this structural appearance in other normal renal tissues, we imaged three additional normal kidney samples. Representative LIV and Fast-DOCT imaging of another normal renal tissue sample is illustrated in Fig. We take the advantage of label-free functional imaging methods to explore whether we can detect any difference in the morphological renal structural changes between normal kidney and kidney subject to obstruction for several days. We imaged 1-week and 2-week obstructed kidney models using the dynamic OCT microscope and then reconstructed the LIV and Fast-DOCT images from the time-sequential OCT frames. Details of the obstructed kidney model is described later in the “Figures The cross-sectional Fast-DOCT of the 1-week obstructed kidney also reveals hyper-Fast-DOCT spots that correlate well with the position of the high LIV Fig. j. These The H &E staining histology images of 1-week and 2-week model kidney tissues are shown in Figs. We further explored the possibility of imaging the contralateral non-obstructed and obstructed kidneys of a single mouse. The left kidney was obstructed by tying its ureter with a nylon thread, and is referred to as the obstructed kidney, while the right kidney on the opposite side is referred to as the non-obstructed kidney.A comparison between the slab average projection images of the contralateral non-obstructed and obstructed kidneys is displayed in Fig. 1. The renal corpuscles contain cluster of capillaries, called the glomeruli, which are surrounded by Bowman’s capsules. The renal tubules consist of the proximal convoluted tubule (PCT), the distal convoluted tubule (DCT), and the loop of Henle. Several previous studies demonstrated renal tubular imaging in animal kidneys using OCT. For example, Chen et al. demonstrated fine superficial renal tubular structural imaging in Munich–Wistar-strain rat kidney ex vivo using a high-resolution OCT  projection image of the normal kidney Figs. , 2 and 315. We found that the diameter of this structure was around 20–25 We determined the diameter of these pipe-like structures from the OCT projection image and compared it with previous OCT measurements15. This can be explained by two reasons. First, the resolution of the OCT system used in this study , and fatty acid transport proteins (FATP)37. We believe that after we sacrifice the mouse, the active transport process stopped in the renal tubule, but the RTECs around the renal tubules were still highly active. The active RTECs can cause intercellular motion resulting in speckle fluctuation, and hence could result in a high DOCT signal around the renal tubule.We suspect that the high DOCT signal in the pipe-like structures originates from active renal tubular epithelial cells (RTECs) around the renal tubule. A schematic of renal tubule function is shown in Fig. 29, we found similar surrounding tissue activity. Specifically, we observed high activity just beneath the liver vessel using LIV imaging, which was due to the high metabolism of the periportal and pericentral regions of the liver microvasculature.We mentioned earlier that the diameter of the pipe-like convoluted structures was larger in LIV than in the OCT image. This can be explained by the previous discussion that what we are observing in the LIV image might be the functional activity of the RTECs around the renal tubule. As the high LIV signals are from the surrounding tissue around the renal tubule, it seems plausible that the diameter of these structures is higher in LIV compared to OCT. Note that, in our previous study on mouse liver38. During the apoptosis or necrosis process, the function of the renal tubule can gradually alter and is finally lost due to cell death. The histology of the 2-week obstructed model also suggests renal structural damage and dilation, which further highlight the process of renal injury due to obstruction. The absence of the pipe-like structures in the LIV and Fast-DOCT images in the obstructed models may therefore indicate the loss of renal tubule function due to the effect of obstruction.Next, we discuss our findings in the obstructed kidney models. The anatomical pipe-like structures that were observed in the LIV of normal kidneys were almost not found in the 1-week model and were completely absent from the 2-week model. Literature suggests that obstruction of the ureter for a certain period results in renal hemodynamic and metabolic changes, followed by tubular injury, and finally apoptosis or necrosisInstead of any anatomical structures, we found a high-LIV superficial layer around the peri-renal cortex region in both obstructed kidney models Figs. , 5. Simi40. Bai et al. reported that several pro-inflammatory cytokine IL-141. Due to regulatory mechanisms involved during renal inflammation, these inflammatory cells , LIV cannot visualize the fine high dynamics structures18. To overcome the above limitation of LIV, we employed complex-correlation-based Fast-DOCT contrast in addition to LIV to visualize structures with high dynamics, and imaging with this contrast did not exhibit any tailed artifacts.In this study, we employed two different DOCT algorithms to access the slow and fast functional activity of renal tubules; one based on logarithmic intensity variance and one on complex correlation analysis of time-sequence OCT intensity. In our pilot study, we detected several vertical artifacts in the LIV cross-section of normal kidneys, similar to the OCT angiography (OCTA) projection artifacts of retinal imaging43.We also note that LIV and Fast-DOCT were reconstructed from two volumes using two different scanning protocols . Because of this strong selective contrast, the interpretation of Fast-DOCT by comparing it with histology is not easy. It might be a future investigation to interpret these high-dynamic spots in Fast-DOCT and correlate them with the histological findings. One additional point is that the utility of the Fast-DOCT contrast could potentially be more helpful when directed toward the examination of in vivo tissue dynamics. In such scenarios, this contrast might enable the capture of certain fast-dynamic signals that could not be detected by the LIV signal.In comparison to LIV, Fast-DOCT specifically contrasts only the regions with fast activities. Hence, it can visualize only some of the renal tubules. Our previous studies of ex vivo liver tissuesSignal-to-noise ratio (SNR) plays a crucial role in the reliability and interpretability of the dynamic OCT contrast. For an accurate interpretation of the dynamic OCT signal, we should consider the regions that show high SNR, where the OCT signal is predominantly high and less affected by noise. For example, the deep tissue regions where the signal is mostly affected by noise appeared as high decorrelated signals in the Fast-DOCT contrast and green in LIV signals. These high-decorrelated signals and high-LIV signals at these regions should be considered artifacts that arise mainly due to low SNR. Note that, out of two DOCT contrasts, the Fast-DOCT signal was computed with noise-offset correction, whereas the LIV contrast was computed without any noise correction method. In the future, a proper noise model will be implemented to obtain a more accurate LIV signal less affected by the noise sources.31 is applied to correct phase instability due to bulk motion before generating the Fast-DOCT contrast. In addition to bulk motion, the hardware of the swept-source OCT system can also introduce phase instability. The phase error can arise due to the asynchronization between A-trigger, which triggers a single spectral acquisition, and the k-clock, which is used for clocking each acquisition point in the spectrum. The mutual shift between the A-trigger and the k-clock induces phase error and hence can corrupt the phase consistency among the volumetric complex OCT datasets. Our previous studies found that the phase remains stable and consistent among the volumetric OCT datasets in our swept-source OCT system32. In conclusion, it is important to consider phase stabilization methods to accurately compute phase-sensitive dynamic OCT contrasts. It is also noteworthy that the LIV uses only OCT intensity, and hence not sensitive to the phase noise.In addition to SNR, maintaining phase stability is also important for calculating phase-sensitive dynamic OCT contrasts like the Fast-DOCT signal. In this study, phase offset correction methodThe functional activity of normal kidney, a 1-week obstructed kidney model, and a 2-week obstructed model, were investigated three-dimensionally and without any labeling by volumetric dynamic OCT technique. LIV and Fast-DOCT imaging successfully visualized superficial pipe-like functional structures in the normal kidney that may correspond to renal tubules. These structures were hardly visible in the 1-week obstructed model and were completely absent in the 2-week obstructed model. The absence of pipe-like structures in obstructed kidney models may suggest the loss of renal tubular function due to the effect of obstruction. Instead of any anatomical structures, a circular shell at the peri-renal cortex region was visualized in the LIV in both 1-week and 2-week obstructed models that was not found in normal kidney. In conclusion, LIV imaging can be used to visualize the functional activity of renal tubules without any exogenous labeling, and hence can be used as a tool to assess and investigate kidney injury in animal models.Experiments were performed on normal mouse kidneys and obstructed mouse kidney models ex vivo. In total, thirteen C57BL/6 mice at an age of 3–5 months were used in the experiments. Sixteen samples of healthy kidney tissue (8 mice), along with three samples from a 1-week obstructed kidney model (3 mice) and two samples from a 2-week obstructed model (2 mice) were used in this study. The mice were housed in normal conditions under a 12-h light/dark cycle.For the normal mouse kidney study, the mice were sacrificed via cervical dislocation and the kidneys were extracted and placed in a petri dish. After dissection, the kidney was submerged in Dulbecco’s modified eagle medium  supplemented with 10% fetal bovine serum  and penicillin/streptomycin , and kept at room temperature during the experiment. OCT imaging was performed almost 1-h after the dissection. After the OCT measurement, the same renal tissue was fixed in paraformaldehyde and stained with H &E.For the obstructed mouse kidney model, mice were anesthetized by a combination of anesthetics  and the left ureter was then exposed by a dorsal incision. The left ureter was obstructed by ligation with nylon sutures and the right ureter was left as it was. Following the surgical procedure, mice were injected with anesthetic antagonist, Atipamezole (Nippon Zenyaku Kogyo). The ligation in the left ureter was applied for either 7 days or 14 days. The kidney of the ligated left ureter is referred to as the obstructed kidney and the contralateral right kidney as the non-obstructed kidney. Obstructed kidney subjected to ureter ligation for 7 days is referred to as the 1-week obstructed kidney model, while kidney subjected to 14 days of ligation is referred to as the 2-week obstructed kidney model. After 7 or 14 days, the mice were sacrificed and the kidneys were extracted and placed in a petri dish. Similar to the previous study on normal kidney, the obstructed and non-obstructed kidneys were submerged in DMEM cultured medium and kept at room temperature during the OCT measurement. H &E staining histology was performed after the OCT measurement.All animal experiments were performed in accordance with the animal study guidelines of the University of Tsukuba. All experimental protocols involving mice were approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Tsukuba. The present study was designed, performed, and reported according to the principles of ARRIVE  guidelines.33. The system uses a MEMS-based wavelength sweeping laser  with a scanning frequency of 50 kHz, a center wavelength of 1.3 33.A custom-made swept-source-based OCT device was used in this studyTwo different signal processing algorithms were used to visualize slow  and fast  dynamics. One is LIV which is sensitive to slow dynamics, and another is complex-correlation-based Fast-DOCT which is sensitive to fast millisecond-scale tissue dynamics.18.x and z are the lateral and axial positions, respectively, i-th frame, N is the number of frames.LIV is the contrast for measuring the OCT signal fluctuation magnitude. The LIV signal was computed using the variance of the time-sequence logarithmic (dB)-scale OCT intensity signal18. In this composite image, the Hue (H channel) is the LIV, the pixel brightness (V channel) is the dB-scale OCT intensity, and the saturation (S channel) is set to 1 for all pixels. In the LIV image, regions with high dynamics appear as green and regions with low dynamics appear as red.The LIV images are shown by pseudo-color composite image as described in Sect. 2.3 of Ref.31; it is identical to the complex correlation-based OCTA. Specifically, we used noise-corrected OCTA, which is a complex decorrelation with a noise offset correction.Fast-DOCT is based on OCT angiography (OCTA) technology, and is computed through complex correlation analysis among the four repeated frames with noise correction25. For the repeating raster scan, the whole volumetric region   after the OCT measurement. The kidneys were then embedded in paraffin, and thin sections of 12 Supplementary Figure 1.


To make this approach viable, we incorporate the circuit's non-ideality, leakage, and process variations into a novel hardware-algorithm co-design framework that leverages extensive HSpice simulations of our proposed circuit using the GF22nm FD-SOI technology node. We verified our framework on state-of-the-art neuromorphic vision sensor datasets and show that our solution consumes ~2× lower backend-processor energy while maintaining almost similar front-end (sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art while maintaining a high test accuracy of 88.36%.Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating  Today's widespread video acquisition and interpretation applications [e.g., autonomous driving  are focused on conventional frame-based imagers. However, many researchers are now exploring the use of event-driven neuromorphic cameras or dynamic vision sensors (DVS)  computing paradigm in which we implement the first spiking CNN layer using embedded transistors that model the multi-bit multi-channel weights and enable massively parallel in-pixel spatio-temporal MAC operations. Because the DVS event spikes are asynchronous in nature, we perform the multiply operation by accumulating the associated weight each time a pixel event occurs. We threshold the accumulated value at the end of each time window to produce a binary output activation and reset the accumulator in preparation for the next time window. To support multiple input filters operating on individual pixels, we parallelize this operation and simultaneously operate on all channels . This charge-based in-pixel analog MAC operation exhibits higher energy efficiency than its digital off-chip counterpart. Moreover, the sparse binary output activations are communicated utilizing a modified address-event representation (AER) protocol, preserving the energy benefit of the workload sparsity. In addition, we have developed a hardware-algorithm co-design framework incorporating the circuit's non-linearity, process variation, leakage, and area consideration based on the GF22nm FD-SOI technology node. Finally, we have demonstrated the feasibility of our hardware-algorithm framework utilizing state-of-the-art neuromorphic event-driven datasets  and evaluated our approach's performance and energy improvement. We incur a ~5% accuracy drop in these datasets because our charge-based P2M approach does not capture the conventional notion of membrane potential for the first CNN layer. This lack of membrane potential is due to the limited time a passive analog capacitor can effectively store charge without significant leakage. However, this problem can be mitigated using non-volatile memories  that natively accept asynchronous input events. Traditionally, time is decomposed into windows, and the number of spikes that occur in each time window is accumulated independently for each pixel creating multi-bit inputs to a spiking CNN. The first spiking CNN layer thus consists of digital MAC operations (not accumulations because the input is multi-bit instead of binary), unlike the subsequent spiking CNN layers that consist of more energy-efficient accumulations that operate on spikes  paradigm for neuromorphic image sensors, wherein multi-bit pixel-embedded weights enable massively parallel spatio-temporal convolution on input events inside the pixel array.We propose a novel neuromorphic-processing-in-pixel-in-memory  units and incorporate the non-idealities and process variations of the analog convolution blocks into our algorithmic framework.Finally, we develop a hardware-algorithm co-design framework considering hardware constraints , benchmark the accuracy, and yield a ~2× improvement in backend-processor energy consumption on the IBM DVS128-Gesture dataset with a ~5% drop in test accuracy.2M approach. Section 3 explains our hardware-algorithm co-design approach and hardware constraints on the first layer of the neural network model. Section 4 demonstrates our experimental results on two event-driven DVS datasets and evaluates the accuracy and performance metrics. Finally, Section 5 presents the concluding remarks.The remainder of the paper is organized as follows. Section 2 describes the circuit implementation, operation, and manufacturability of our proposed Neuromorphic-P2M approach. 2M architecture. The top die consists of DVS pixels and generates ON (OFF) events based on the increase (decrease) in input light contrast level. A DVS pixel consists of a logarithmic receptor, source-follower buffer, capacitive-feedback difference amplifier, and two comparators  operations in the analog domain for a fixed period of time. After that, the final accumulated voltage of each kernel is compared with a threshold voltage to  generate the output activation spike for the next layer.Read Phase: Finally, during the read phase, the output activations of different kernels are sequentially read utilizing the asynchronous Address-Event Representation (AER) read scheme.More details on each step, including their hardware implementations, will be explained below.As illustrated in 2M approach.Note that the weights cannot be re-programmed after manufacturing. However, it is common to use pre-trained weights for the first few layers as low-level feature extractors in modern neural network models  as a function of the normalized weight transistor's To incorporate the circuit's non-ideality in our algorithmic model, we have simulated the output characteristics of the positive and negative weights for the different numbers of input event spikes using the GF 22nm FD-SOI node. spatio-temporal MAC operations across multiple channels simultaneously for each kernel. 2M architecture. The left sub-figure represents an array of DVS pixels (each white rectangular box includes multiple DVS pixels arranged in rows and columns) consisting of multiple channels distributed spatially. Each DVS pixel is connected with multiple weight transistors of the analog MAC blocks depending on the number of channels and stride . Each channel performs analog MAC operations asynchronously for a fixed temporal window . For instance, assume the kernel size is 3 × 3, and each kernel has 5 different channels that are represented by the white rectangular boxes in the left sub-figure. The right sub-figure exhibits the zoomed version of the 3 × 3 kernel with 5 different channels. Each channel has a dedicated accumulation capacitor  and a local bitline so that charge can accumulate across all the different channels at the same time. Depending on the kernel size, multiple weight transistors (both positive and negative) are connected to its kernel-dedicated accumulation capacitor using the local bitline of each channel. In this example, 18 weight transistors (kernel size = 3 × 3 and for the ON and OFF channels of the DVS pixels) are connected with a single kernel capacitor. The per-channel accumulation capacitor and local bitline shared among the kernel's weight transistors enable simultaneously and massively parallel spatio-temporal MAC operations across different channels. The multiplication results  accumulate on the kernel capacitor for a fixed temporal window . These analog MAC operations are asynchronous and parallel across all the kernels for all the input feature maps (DVS pixels) throughout the sensor array. Finally, a thresholding circuit compares the final accumulated voltage on each channel's capacitor with a reference voltage to generate the output activation spike. Output activations from different channels are multiplexed  to communicate with the AER read circuits at the periphery (left sub-figure) through the kernel-level AER logic block (right sub-figure). The row request (RA) and row acknowledge (RA) signals are shared along the rows, and the column request (CR) and column acknowledge (CA) signals are shared along the columns. After the read operation (described in Section 2.3), the kernel's accumulation capacitor is reset to 0.5V_DD by the reset transistor (M_RST) shown in In the first spiking CNN layer, we must perform The frequency of the reset operation is based on the amount of time the capacitor can hold the charge without significant leakage. To minimize the capacitor leakage, we use high-V_T weight transistors, a switching transistor (M_SW) to disconnect the kernel's capacitor from the weight transistors, and kernel-dependent nullifying current source (I_NULL) shown in . Accordi2M using the GF22nm FD-SOI technology node considering random inputs and weights. For this simulation, a kernel size of 3 × 3 has been considered. The weights, the instant of the events, and the number of events per DVS pixel are generated randomly. For the test HSpice simulation, 1 ms simulation time has been considered according to our algorithmic framework; hence, all the output events from DVS pixels within this time period will be multiplied with their weights and accumulated on the Kernel's accumulation capacitor before being compared with a fixed threshold voltage. The top subplot exhibits that the DVS pixels  are generating the event spikes at different time instants. PX_13, PX_21, PX_23, PX_32 are connected with positive weights, whereas the other pixels are connected with negative weights. It may also be noted that a few pixels  do not generate any event during this time frame. This test simulation also considers these no-event generation scenarios to mimic the actual dataset sparsity. From the bottom subplot, it can be observed that the convolution output (V_CONV) of our analog MAC circuit is updating (charging or discharging) for each input event spike. When the weight is positive (negative), the accumulation voltage steps up (down) depending on the weight value. Finally, after the fixed temporal window, the convolution output has been compared with the threshold voltage. If the convolution output is higher than the threshold voltage, the comparator will generate an output activation spike (V_ACT) for the next layer for each kernel.2M paradigm). The representative read scheme is illustrated in 2M architecture can support multiple numbers of channels  as required by the spiking CNN model. The outputs of the channels (thresholded output activation spikes) are read sequentially throughout the P2M array in an asynchronous manner. At a time, one channel is being asserted of the P2M array by activating V_Ki sequentially, where i = 1, 2, ... N_C , similar to the base DVS systems. As a result, the required number of address bits that need to be communicated off-chip has been reduced from the base DVS system. Hence, our P2M architecture maintains the energy benefit of a sparse system due to utilizing the AER read scheme along with lower off-chip communication energy cost due to generating fewer address bits per output activation.In this sub-section, we propose modifications to the standard AER scheme in a manner so that it can be compatible with the presented asynchronous processing in-pixel computations. We are utilizing the asynchronous AER read-out scheme Boahen,  to read shown in . Kernel-shown in . The per2M paradigm. Our proposed system can be divided into two key dies, i) a backside illuminated CMOS image sensor (BI-CIS) consisting of the DVS pixels and biasing circuitry, and ii) a die containing multi-bit multi-channel weight transistors, accumulation capacitors, comparators, and AER read circuits. m × 40 μm  in a non-linear manner in such a way that the output accumulation voltage steps can increase or decrease linearly for positive and negative weights, respectively. However, the nonlinearity is also a function of the drain-to-source voltage of the weight transistors. In our scheme, we are charging or discharging the kernel's capacitor during the computation phase. Depending on the weight values, the charging and discharging current are functionally dependent on the drain-to-source voltage. Hence, when the accumulation voltage on the V_OUT node  will not incur any significant accuracy issues for these 1,000 random tests. Among 1,000 random tests, only 100 test results (for clear visibility) are shown in To validate our Hspice simulations generated curve-fitting function's prediction accuracy, we have tested 1,000 random cases. In these test cases, we have used a kernel size of 3 × 3, where the weight values are generated randomly. Moreover, the number of input event spikes and time instants for the input spikes are also randomly generated. Note these random tests utilize 100 μ s of simulation time for the convolution phase to reduce the total simulation time. As mentioned earlier, utilizing kernel-dependent nullifying current source, high-2M architecture, we have utilized a kernel-dedicated capacitor to enable instantaneous and massively parallel spatio-temporal convolution operation across different channels. We need a kernel-dedicated capacitor to preserve the temporal information of input DVS spikes across different channels simultaneously. Moreover, there is a direct trade-off between the acceptable leakage and capacitance value . Almost 47% of the area in our P2M array is occupied by the capacitors. Hence, we have reduced the number of channels in our spiking CNN models compared to the baseline neural architecture not to incur any area overhead while preserving the model accuracy. In addition, the leakage also limits the length of each algorithmic time step in our algorithmic framework. We have also reduced the time length in our neural network model to minimize the kernel-dependent leakage error of our custom first convolution layer. Moreover, to reduce the amount of data transfer between the P2M architecture and the backend hardware processing of the remaining spiking CNN layers, we have avoided the max pooling layer and instead used a stride of 2 in the P2M convolutional layer. Lastly, we incorporate the Monte Carlo variations in the proposed non-linear custom convolutional layer explained above in our algorithmic framework. In particular, we have estimated the mean and standard deviation of the output of the custom convolutional layer from extensive circuit simulations. We then train our spiking CNN with the addition of the standard deviation as noise to the mean output of the convolutional layer. This noise addition during training is crucial to increase the robustness of our spiking CNN models, as otherwise, our models would incur a drastic drop in test accuracy.In our proposed neuromorphic-P2M paradigm on complex neuromorphic datasets where each event is at least a few seconds long. Hence, with a timestep length of 1 ms, we will require more than thousands of total time steps to train our SNNs with these neuromorphic datasets. This is impractical (it would require more than a year to train one SNN model on the DVS Gesture dataset) in modern GPUs typically used for training SNNs. To mitigate this problem, we only employed a small timestep length (1 ms) in our first P2M-implemented layer where the weights are kept frozen while the remaining layers implemented outside the sensor are trained with a large timestep length that leads to a small number of total time steps. The weights in the P2M-implemented layer are obtained from a baseline SNN where all the layers have the same large timestep length. Thus, the length of the timestep impacts the trainability of the SNNs. It also affects the temporal information injected into the SNN, i.e., as the length of the timestep reduces, the SNN can extract more fine-grained temporal features, which can potentially improve the inference performance at the cost of reduced trainability. We expect to reduce energy consumption by increasing the time step length as that would inject a smaller number of spikes into the network .In this work, we have evaluated our P2M for event-driven neuromorphic tasks where the goal is to classify each video sample captured by the DVS cameras. In particular, we evaluate our P2M approach on two large-scale popular neuromorphic benchmarking datasets.This article focuses on the potential use of PThe IBM DVS128-Gesture  without any P2M circuit constraints. We then decrease the integration time of the first spiking convolutional layer for P2M implementation and integrate the spikes in the second interval such that the network from the second layer processes the input with only a few time steps. We fine-tune this network from the second layer while freezing the first layer since training the first layer significantly increases the memory complexity due to a large number of time steps. This is because the gradients of the first layer need to be unrolled across all the time steps.For these datasets, we apply a 9:1 train-valid split. We use the Spikingjelly package , hardly incur any additional drop in accuracy. Overall, our P2M-constrained models lead to an average 5.2% drop in test accuracy across the two datasets.We evaluated the performance of the baseline and P2M-implemented spiking CNN models for neuromorphic datasets. Note that we do not evaluate the latency of our models since that would depend heavily on the underlying hardware architecture and data flow of the backend hardware . The frontend energy (Efrontend) is comprised of sensor energy (Esens) and communication energy (Ecom), while the backend energy (Ebackend) to process the SNN layers (excluding the first layer for the P2M implementation) is primarily composed of the accumulation operations incurred by the spiking convolutional layers (Eac) and the parameter read (Eread) costs. Assuming T denotes the total number of time steps and s denotes the sparsity. The energy components can be approximated as follows:We develop a circuit-algorithm co-simulation framework to characterize the energy consumption of our baseline and Peevent represents per-pixel sensing energy, Nevent denotes the number of events communicated from the sensor to the backend, and Ebias is the biasing energy for the DVS pixel array considering the dataset duration. In addition, esens−to−tx is the communication energy to send the address bits from the sensor node to the transmitter, and etx is the wireless transmission energy to the backend. Note that the first convolutional layer of the SNN in the baseline implementation requires MAC operations, and hence, we need to replace eac with the MAC energy emac and use s = 1. For a spiking convolutional layer that takes an input Nac , which consume ~32× more energy compared to cheap accumulate operations  while also significantly reducing energy-consumption of MAC operations by utilizing massively parallel non-von-Neumann analog processing-in-pixel.The energy values we have used to evaluate n et al. , and eacg et al. . Figure in-situ massively parallel charge-based analog spatio-temporal convolution across the pixel array. Moreover, we have incorporated the hardware  constraints of our analog computing elements as well as area consideration  into our algorithmic framework. Our P2M-enabled spiking CNN model yields an accuracy of 88.36% on the IBM DVS128-Gesture dataset and achieved ~2 × backed energy reduction compared to the conventional system.We have proposed and implemented a novel in-pixel-in-memory processing paradigm for neuromorphic event-based sensors in this work. To the best of our knowledge, this is the first proposal to enable massively parallel, energy-efficient non-von-Neumann analog processing-in-pixel for neuromorphic image sensors using novel weight-embedded pixels. Instead of generating event spikes based on the change in contrast of scenes, our proposed solutions can directly send the low-level output features of the convolutional neural network using a modified address event representation scheme. By leveraging advanced 3D integration technology, we can perform https://research.ibm.com/interactive/dvsgesture/, https://www.garrickorchard.com/datasets/n-mnist.Publicly available datasets were analyzed in this study. This data can be found here: 2M for the neuromorphic vision sensor and developed the corresponding circuit simulation framework. GD developed the baseline and P2M-constrained algorithmic framework with the help of ZW. APJ and ARJ proposed the idea of P2M. MK and GD wrote the majority of the paper. ARJ and PAB supervised the research and reviewed the manuscript extensively. All authors reviewed the manuscript. All authors contributed to the article and approved the submitted version.MK proposed the use of P


Several new treatment combinations have been approved in metastatic renal cell carcinoma (mRCC). To determine the optimal therapy on the basis of cost and health outcomes, we performed a cost-effectiveness analysis of approved immunotherapy-tyrosine kinase inhibitor/immunotherapy drug combinations and sunitinib using public payer acquisition costs in the United States.We constructed a decision model with a 10-year time horizon. The seven treatment drug strategies included atezolizumab + bevacizumab, avelumab + axitinib, pembrolizumab + axitinib, nivolumab + ipilimumab (NI), nivolumab + cabozantinib, lenvatinib + pembrolizumab, and sunitinib. The effectiveness outcome in our model was quality-adjusted life-years (QALYs) with utility values on the basis of the published literature. Costs included drug acquisition costs and costs for management of grade 3-4 drug-related adverse events. We used a partitioned survival model in which patients with mRCC transitioned between three health states  at monthly intervals on the basis of parametric survival function estimated from published survival curves. To determine cost-effectiveness, we constructed incremental cost-effectiveness ratios (ICERs) by dividing the difference in cost by the difference in effectiveness between nondominated treatments.The least expensive treatment was sunitinib , whereas the most expensive was either lenvatinib + pembrolizumab or pembrolizumab + axitinib . NI yielded the most QALYs (3.6), whereas avelumab + axitinib yielded the least (2.5). NI had an incremental ICER of $297,465 USD-$348,516 USD compared with sunitinib. In sensitivity analyses, this ICER fell below $150,000 USD/QALY if the initial 4-month cost of NI decreased by 22%-38%.NI was the most effective combination for mRCC, but at a willingness-to-pay threshold of $150,000 USD/QALY, sunitinib was the most cost-effective approach. These novel immunotherapy (IO)-based drug combinations directed against the programmed cell death (programmed cell death protein 1-programmed death-ligand 1) receptor axis and also combined with vascular endothelial growth factor receptor-tyrosine kinase inhibitors (TKI) or with other IO drugs (immunotherapy-immunotherapy [IO-IO]) were compared with single-agent sunitinib, a vascular endothelial growth factor receptor-TKI first approved for treating mRCC in 2006, and demonstrated significantly improved response rates, progression-free survival (PFS), and overall survival (OS).2The estimated incidence of renal cell cancer in the United States will be 79,000 with an estimated 13,900 deaths in 2022.3 Costs of management of treatment toxicities can include outpatient management of rash and hypo- and hyperthyroidism to inpatient hospitalizations secondary to pneumonitis, colitis, and diarrhea. Management of these drug combinations can also include a 14% chance of having to deliver high-dose corticosteroids.3 These increased management costs of drug delivery and the impact on quality of life of patients undergoing first-line IO-TKIs or IO-IO combinations for mRCC should be weighed against the demonstrated gains in survival when considering the most optimal cost-effective treatment choice.However, a significant proportion of patients receiving these novel drug combinations have treatment-related adverse events (trAEs) and/or immune-related adverse events (irAEs). In one systematic review, the pooled incidence of adverse events in patients with mRCC undergoing contemporary drug treatments with IO and IO-TKI combinations was 88.3% for any-grade trAEs, 42.7% for any-grade irAEs, 36.1% for grade ≥ 3 trAEs, and 11.1% for grade ≥ 3 irAEs, whereas the overall mortality from drug-related toxicity was 0.9%.4 pembrolizumab + axitinib ,5 avelumab + axitinib ,6 nivolumab + cabozantinib ,7 lenvatinib + pembrolizumab ,8 and AB .9 Since all the above novel drug combinations were compared with sunitinib in these trials, sunitinib was used as a reference for conducting the CEA .We conducted a cost-effectiveness analysis (CEA) of five recent drug combination approvals in the management of mRCC in the first-line setting from randomized clinical trials and a sixth first-line treatment regimen . These included nivolumab + ipilimumab ,We developed a decision analytic model to describe the treatment course of patients diagnosed with mRCC using costs from the perspective of two US public payers: the Department of Veterans Affairs (VA) and the Centers for Medicare & Medicaid Services (CMS). We estimated the cost and effectiveness of the seven first-line treatments for mRCC: (1) AB, (2) AA, (3) PA, (4) NI, (5) NC, (6) LP, and (7) sunitinib. The effectiveness measure was quality-adjusted life-years (QALYs), a metric that encompasses both duration and quality of life. Costs included the direct costs of the drug and administration and a set of drug adverse events (ADEs) for each treatment. Each model cycle represented 4 weeks, and the time horizon of the study was 10 years, as the life expectancy of patients with mRCC rarely crosses 10 years. We applied a half-cycle correction and a 3% annual discount rate for both costs and effectiveness outcomes. The model was programmed in TreeAge Pro 2018 , and statistical analyses on survival functions were performed in R .12 The model included three health states: progression-free, progression, and death , a commonly used approach for CEA studies in oncology.16 and WebPlotDigitizer (version 4.5)17 software. To extrapolate mortality data to a 10-year time horizon, we applied an algorithm provided by Hoyle and Henley18 and generated pseudoindividual patient events and censoring data. Parametric survival functions for both PFS and OS were chosen on the basis of goodness-of-fit statistics (Akaike information criterion) among potential distributions: exponential, Weibull, logistic, lognormal, and log-logistic. In our study, the survival functions followed exponential, log-logistic, or Weibull distributions (Data Supplement). Since each of the six trials for new drugs included sunitinib as the comparator, we had six different sunitinib median PFS periods reported in the study-specific Kaplan-Meier curves. We ran our model separately using the highest (CheckMate 214 [NI v sunitinib]) and lowest  reported median PFS among the six trials.To extract the probability coordinates from the published Kaplan-Meier PFS and OS curves in the trials, we used Engauge Digitizer (version 12.1)19 and were applied in the first cycle of the model. Additional treatment cost of ADEs for sunitinib was applied for those who received the subsequent treatment. The list of ADEs and their likelihood and treatment costs for each first-line treatment are summarized in the Data Supplement.We included ADEs that were either grade 3 or 4 and were reported for at least 5% of patients for each first-line treatment from each trial. Treatment costs for each ADE were derived from Healthcare Cost & Utilization Project and other sources22 Among IO combinations, the PFS utility values ranged from 0.87 for NC and LP on the basis of their efficacy in terms of median PFS15 to 0.77 for PA,20 whereas the utility score for sunitinib was 0.7321 (Data Supplement). The utility score for the progression health state was estimated at 0.1 less than the utility score for progression-free state for each first-line treatment.22QALYs were calculated in the model by weighting the time spent in each health state by health-related quality of life, as measured by a utility score, which ranges from 0 (death) to 1 . Utility scores for each first-line treatment were derived from previously published data.23 The direct costs and dosages of each drug for the seven first-line treatment are summarized in the Data Supplement. Average administration costs of $74.40 US dollars  and $153.60 USD , derived from the CMS physician fee schedule, were added to the total costs for drugs that require injection administration. Per-cycle per-treatment cost estimates are summarized in the Data Supplement.We included drug costs of the first-line treatments from two different sources representing different US public payer perspectives: VA and CMS. For the VA perspective, we used the Federal Supply Schedule (FSS) pharmaceutical pricing data, which reflect negotiated prices between drug companies and VA, which is one of the largest purchasers of pharmaceuticals in the United States. For the CMS perspective, the direct Medicare/Medicaid drug costs were derived from the Medicare Part B Drug Average Sales Price (ASP) reported from the drug manufacturers without a Medicaid drug rebate agreement and the National Average Drug Acquisition Cost (NADAC) when ASP was not available. The ASP or NADAC was available for atezolizumab, avelumab, pembrolizumab, nivolumab, bevacizumab, and sunitinib. For the remaining drugs , we calculated the CMS drug cost by multiplying the FSSTable cost by the ratio of CMS to FSS cost from the drugs for which the CMS cost was available. All direct drug costs were calculated on the basis of 71.4-kg patient.24 Monitoring costs included monthly general practitioner visit and blood test and biannual computed tomography scans, and costs for best supportive care postprogression included general practitioner visits, community nurse visits, pain medications, and postdiscontinuation treatments. We assigned monitoring costs for all patients who underwent any treatment options whereas postprogression costs for patients who had progression of the disease or were at the end of treatments, but did not receive the second-line therapy. Cost of the final month of life was assigned at death. Monthly monitoring, postprogression, and end-of-life costs are summarized in the Data Supplement. All costs were adjusted to 2021 USD using the Personal Consumption Expenditures price index for health care services.Monitoring, post-progression, and end-of-life costs were based on the secondary literature.Point estimates for each input parameter were used in the primary analysis. We also performed one-way sensitivity analyses on the cost of medication to identify the threshold at which more costly treatments would become cost-effective at a willingness-to-pay (WTP) threshold of $150,000 USD/QALY. Deterministic sensitivity analyses on drug and ADE treatment costs and utilities with 20% variation  were performed to identify high-impact parameters in the model. We did not conduct probabilistic sensitivity analyses because of the lack of data regarding the variation of input parameters.The results from the primary analysis showed that 10-year total costs—inclusive of the medications, their administration, and ADEs—were the lowest for sunitinib  and highest for either LP or PA , regardless of the perspective and the rate of subsequent therapy (Table The results of the one-way sensitivity analysis on the direct cost of NI are illustrated in Figure 25 In the absence of validated companion biomarker-based guidance for individual approved therapeutic combinations, the determination of cost-effectiveness has become an important tool to pursue for individualizing drug treatment choices. We attempted to evaluate comparative costs and clinical impact of all approved standard-of-care drug combinations in the first-line setting used currently for treating mRCC. We used published randomized clinical trial survival and adverse effects data and estimated cost-effectiveness for a 10-year horizon. We also assumed two different clinical scenarios of therapeutic interventions in the second-line treatment setting. These basic assumptions are in line with currently accepted clinical practice and the methods used for economic evaluations in oncology12 including the preferred use of PSA in cost evaluation strategies when evaluating novel IO-based drug therapies.27mRCC management has seen a rapid evolution of the therapeutic landscape since 2017 with the introduction of several novel immune checkpoint inhibitors and targeted therapy combinations of both classes of drug agents. Immunotherapeutic-based drug combinations that have emerged as first-line choices have increased response rates and longevity of life and provided a fundamental shift from interferon and interleukin-based interventions. However, these contemporary therapies lack validated predictive biomarkers to guide choices in individual patients, and although response rates have increased with these combinations, not all patients have clinical benefit and all drug regimens are associated with significant side effects and financial toxicity. In fact, higher drug costs have been widely reported across the spectrum of cancer drug treatments and the majority of newly approved anticancer molecular entities lack validated cancer biomarkers for identifying patient subcohorts most likely to benefit within the same tumor type.28 because of lower drug-associated costs compared with all other approved IO-IO and IO-TKI combinations. Among combination therapies, NI was the only strategy not dominated by sunitinib. NI was associated with higher QALYs over a 10-year time horizon versus all other options including sunitinib. With higher WTP thresholds, NI was observed to be the most cost-effective treatment strategy. The results were consistent whether we used VA FSS or ASP/NADAC drug costs or whether our model assumed that only some or all patients received second-line therapy after failure of first-line treatments.Our results demonstrate that sunitinib was the most cost-effective first-line strategy at a WTP of $150,000 USD/QALY, which is the threshold typically used by the US Institute for Clinical and Economic Research15 found that NI was cost-effective compared with PA and AA; however, in the study, the authors did not include sunitinib as a comparator. Zhu et al29 and Lu et al30 did not find PA or AA to be cost-effective relative to sunitinib. In several studies, PA was reported as being cost-effective compared with NI and/or sunitinib32 and NI was reported as cost-effective as sunitinib.13 A key difference between our analyses and recent publications was the assessment of drug costs. Many of these recent published studies used the drug manufacturer's list price or wholesale acquisition cost, which does not reflect the actual cost paid for drugs because it does not include discounts and rebates. For this reason, Levy et al33 proposed to use either the NADAC and VA Federal Supply Schedule Service or a combination of the two, which we used to reflect the drug supply system. Another major factor that could explain the difference between our findings and those from previous studies is the rate of subsequent therapy. We ran a version of our model in which all patients received second-line therapy and then another with only 30%. Other studies mostly applied around 30% of subsequent therapy on the basis of the reports from clinical trial, which might not reflect the actual clinical practice as second-line treatments are now well embedded in clinical practice. In addition, although there have been many published CEAs already of first-line treatments in mRCC, our study is comprehensive in that it considers all seven first-line approved treatments, whereas others have included a subset of these therapies.Our results differ from recently published cost-effective studies for mRCC on the basis of study design and analyses. For instance, Shay et al34 Finally, we did not conduct probabilistic sensitivity analyses because we did not have information on the distribution of most input parameters. Adding this would provide more information as to the consistency of our results across a wide range of potential values for input parameters.Our study is not without limitations. First, uncertainty is always present when extrapolating survival data to a 10-year time horizon. To reduce this uncertainty, we tested various potential parametric survival functions to find the distribution that most accurately captures the unique characteristics of data in our study. Second, to compare seven different treatments, we used six independent clinical trials that compared each IO-TKI combination with sunitinib. The Kaplan-Meier PFS and OS curves for sunitinib vary across each of these studies. To incorporate this variation into our analysis, we ran our model separately using both the highest and the lowest reported median time for PFS across these six trials. Third, we focused our analysis for the overall patient population from these trials. We did not conduct subgroup analyses in patients from different prognostic groups because a priori enrollment of patients in these prognostic group was not reported consistently in the six clinical trials. Fourth, although the drug cost estimates from VA Federal Supply Schedule Service or ASP/NADAC represent the actual drug costs better than the wholesale acquisition cost, there still exists a large variation in drug costs for different payers across the United States. For example, costs from the private payer perspective were not considered in our study. In addition, we did not include patient out-of-pocket costs, but they may be important as evidenced in previous studies.In conclusion, using a partitioned survival model parameterized using inputs from recent clinical trials, we found that NI yielded the most QALYs of the seven treatments considered. However, despite being the most effective first-line strategy, the treatment, administration, and ADE costs of NI were high enough that sunitinib was the most cost-effective treatment strategy in our model at a WTP of $150,000 USD/QALY.


Background: The natural day-night cycle synchronizes our circadian rhythms, but modern work practices like night shifts disrupt this pattern, leading to increased exposure to nighttime light. This exposure is linked to various health issues. While some studies have explored the effects of night shifts on human circadian rhythms, there is limited research on the consequences of long-term exposure to shift-work light conditions. Rodents can provide valuable insights into these effects. This study aimed to examine how short- or long-term exposure to rotating shifts and chronic jetlag affects the core circadian oscillators in the liver and skin of mammals.Methods:C57BL/6J male mice were subjected to simulated shift-work light conditions, including short-term or long-term rotating shifts and chronic jet-lag conditions. Liver and skin samples were collected every four hours over a 24-hour period on the second day of constant darkness. RNA was extracted and qRT-PCR analysis was conducted to measure the circadian gene expression in liver and skin tissues. Circadian rhythm analysis using CircaCompare compared the control group to mice exposed to shift-work light conditions.Results: The liver's circadian clock is significantly altered in mice under long-term rotating shift conditions, with a lesser but still noticeable impact in mice experiencing chronic jetlag. However, short-term rotating shift conditions do not significantly affect the liver's circadian clock. Conversely, all three simulated shift conditions affect the skin's circadian clock, indicating that the skin clock is more sensitive to shift-work light conditions than the liver clock. Compared to the liver, the skin's circadian clock is greatly affected by long-term rotating shift conditions.Conclusions: The study findings indicate more pronounced disturbances in the canonical clock genes of the skin compared to the liver under simulated shift-work light conditions. These results suggest that the skin clock is more vulnerable to the effects of shift-work. The advent of electricity has led to 24/7 work practices including night shift and rotating shift schedules, increasing nighttime light exposure in modern society. Unfortunately, these modern work habits have been associated with an elevated risk of various health conditions, including cancer, cardiovascular diseases, depression, diabetes, gastrointestinal problems, and metabolic syndrome.– The disruption of circadian rhythms, both at the physiological and behavioral level, contributes to these modern work habit-associated health conditions. The few studies investigating the impact of night shift on the molecular circadian rhythms in humans have revealed significant changes in the circadian transcriptome and metabolome within a one-week study duration.– However, in real-world scenarios, many shift workers experience prolonged exposure to rotating shifts that extend beyond a single week. Unfortunately, there is a lack of systematic research investigating the consequences of long-term exposure to shift-work light conditions on molecular circadian rhythms in humans. This absence may be attributed to logistical, financial, and facility constraints in setting up human sleep laboratories. Nonetheless, rodents can serve as valuable preliminary models for understanding the effects of different shift schedules or exposure to shift-work light conditions on molecular circadian rhythms, providing insights into the underlying molecular mechanisms associated with shift-work related disorders.The natural day-night cycle is the primary synchronizer of circadian rhythms in humans.– have employed these protocols in rodents to investigate the mechanisms underlying tumor development, metabolic disorders, and other health outcomes associated with shift work. These studies have indicated that exposure to shift-work or jet-lag light environments can promote tumor progression,,,– glucose intolerance,, obesity,, genotoxic stress mediated cardiovascular toxicity, and radiodermatitis. Although some research has shown abnormal activity/sleep rhythms in response to shift conditions, our understanding of how short- or long-term exposure to shift-work and chronic jet-lag conditions affects the mammalian core circadian oscillator, the canonical clock genes, remains limited. Rodents serve as a convenient model for studying tissue-specific circadian gene expression of these clock genes. Therefore, in this study, we utilized a well characterized circadian mouse model, C57BL/6J, to investigate the impact of short- or long-term exposure to rotating shifts and jet-lag conditions on the mammalian core oscillators in various tissues.In laboratory settings, researchers can replicate shift-work-like light environments using rotating shift and jet-lag protocols in rodents. Several studies The liver plays a crucial role in carrying out various metabolic functions regulated by the circadian clock through transcriptional and posttranslational mechanisms.– On the other hand, although the skin is not as metabolically active as the liver, it serves as accessible window for understanding the body’s internal clock in humans., Moreover, a study has shown that restricted feeding altered the phase and amplitude of clock genes in mouse skin, subsequently affecting the skin’s response to ultraviolet radiation exposure. Given these factors, the liver and skin represent distinct metabolic tissue models with robust circadian clocks, offering valuable insights into circadian rhythm disturbances caused by shift work.The primary focus of our study was to examine the effects of shift-work-like light conditions on the liver and skin. We specifically chose the liver because it is widely recognized as the primary organ model for studying the circadian clock in peripheral tissues.Arntl, Npas2, Per1, Per2, Per3, Cry1, Cry2, andDbp) in liver and skin samples. The measurements were taken over a 24-hour period on the second day of constant darkness  of Washington State University - Spokane. To guarantee proper animal welfare, the guidelines set by the Institutional Animal Care and Use Committee (IACUC) were followed. Our program compiles with all regulations outlined in the NIH guide for the Care and Use of Laboratory Animals, encompassing procurement, conditioning and quarantine, housing, management, veterinary care, and carcass disposal. All efforts were made to ameliorate the suffering of the mice involved. There were no adverse events during the study and no humane endpoints were established. The animal experiments conducted in this study were approved by the IACUC under the protocol number: 6202 on March 27ad libitum feeding. Throughout the time-course sample collection, red light was utilized, and animals were killed per housed group, where one cage of 3 mice represented one time-point. This was done to limit variability in any time-point. Mice were anesthetized with 3-4% isoflurane and euthanized by cardiac puncture blood collection followed by cervical dislocation as a secondary method of euthanasia. The collected liver and skin tissues were snap-frozen and stored at -80 °C for subsequent RNA extraction.This investigation took place from May through September 2019. Healthy immunocompetent wild-type C57BL/6J male mice at 3 weeks of age were purchased from the Jackson Laboratory (strain #: 000664). Mice were purchased to limit variability and maintain consistency in the genetic quality of the animals. Upon arrival, these mice were acclimatized to the controlled environment of the PLAR facility at Washington State University – Spokane for at least one week. The only inclusion criterion was for animals to be at least 4 weeks of age by day 1. To investigate the impact of shift-work light conditions on circadian rhythms of canonical clock genes, a group of C57BL/6J male mice were randomly assigned and housed (3 mice per cage) in a controlled environment that simulated shift-work light conditions. The mice were subjected to different simulated conditions: either short-term (15 days) or long-term (45 days) rotating shifts, where the light conditions changed every seventh day, or a jet-lag condition lasting 21 days with an 8-hour phase advance every two days. As a comparison, a control group of mice followed the standard light-dark cycle of the facility . A total2O. To assess the quantity and quality of the extracted RNA, we utilized NanoDrop® 2000 Spectrophotometer, measuring the light absorbance at 260, 280 and 230 nm. Following the RNA purification step, we treated the RNA samples with DNase-I , and synthesized complementary DNA (cDNA) using TaqMan™ Reverse Transcription Reagents . Real-time polymerase chain reaction (RT-PCR) was then performed using PerfeCTa® SYBR® Green Supermix with ROX™  in a StepOnePlus™ Real-Time PCR System (Applied Biosystems™). The RT-PCR reaction included denaturation (3 minutes at 95 °C) and 40 PCR cycles (15 sec at 95 °C and 1 minute at 60 °C). The expression level of each clock gene was normalized to the geometric mean expression of two housekeeping genes,Actb andPpib using the ΔCt method. Subsequently, the data were scaled to the sample with the highest expression level (ΔΔCt) for the time course analysis. Missing values in the raw data files resulting from technical issues with the qPCR plate were excluded from analysis. We specifically measured the expression of canonical clock genes  in both liver and skin tissues using validated primers. To ensure the accuracy of the PCR amplification, the primers were validated using cDNA template serial dilution, and their efficiency was calculated based on the slope value . After thorough mixing, 0.2 ml of chloroform was added to the TRIzol tissue cocktail, mixed well, and incubated at room temperature for 3 mins. We separated the different phases of the mixture through centrifugation at 12,000 g for 15 mins. The aqueous phase (top layer) was carefully collected and transferred to a new tube. To precipitate the RNA, an equal volume of isopropanol was added to the collected aqueous phase and mixed by inverting. After a 10-minute incubation at room temperature, the samples were subjected to another round of centrifugation at 12,000 g for 10 minutes. The supernatant was discarded, and the RNA pellet was washed three times with 70% ice-cold ethanol. Subsequently, the RNA pellet was air dried for 5-10 mins and dissolved in 20 μl of ddHvalue . The pri Circadian time, CT0 was defined as the onset of the light phase before the animals entered constant darkness for tissue collection. This alignment allowed us to effectively compare and assess the variations in circadian rhythms between control and simulated shift-work light conditions. We applied a p-value cut-off of<0.05 to determine the presence or absence of rhythms and to evaluate significant differences in phase, amplitude, and the Midline Estimating Statistic of Rhythm (MESOR). Sample resolution is a critical factor in circadian analysis as it directly affects the precision of phase estimates. In our study, we used four-hour sample resolution, therein phase differences were only considered if they were at least four hours apart. This approach was implemented to ensure the accuracy of our results.To analyze the differences in circadian rhythms of canonical clock genes in animals exposed to both control and simulated shift-work light conditions, we utilized the standard parameters of the CircaCompare R package with a 24 hour fixed period.Arntl, Npas2, Per1, Per2, Per3, Cry1, Cry2, andDbp) in the liver samples of mice exposed to simulated shift-work light conditions. Rhythmic gene expression and its significance was determined atp < 0.05 of CircaCompare. Our findings revealed significant rhythmic patterns in all analyzed clock genes, both in the control group and in the mice exposed to the simulated shift-work light conditions. However,Cry1 did not show significant rhythmicity in the long-term rotating shift scenario andCry2 did not exhibit significant rhythmicity in the chronic jet-lag scenario . This suggests that these genes may be more sensitive to prolonged disruption of light-dark cycles. Furthermore, we examined various circadian properties such as phase, amplitude, and MESOR and compared differences between conditions . In the short-term rotating shift and chronic jet-lag scenarios, we did not observe any discernible phase differences compared to the control condition. However, in the long-term rotating shift scenario, significant phase advance of at least four hours were detected in five out of eight clock genes:Npas2,Per1, Per2, Per3, andCry2. Regarding amplitude, we did not find significant differences in the rotating shift conditions. However, we did observe a significant decrease in amplitude ofPer1 andDbp among mice exposed to chronic jet-lag compared to the control group. This indicates that chronic disruption of light-dark cycles may affect the robustness of circadian rhythms in these genes. We did not find any significant changes in MESOR with short-term rotating shift. However, we observed a significant increase in MESOR with the genesArntl, Per2, Per3, andCry2 in the long-term rotating shift condition. Conversely, we found a significant decrease in MESOR with the genesPer1 andDbp in the chronic jet-lag scenario. Overall, these results suggest that the circadian clock in the liver is highly impacted in mice exposed to the long-term rotating shift condition with a clear but less dramatic impact in mice exposed to chronic jet-lag. In contrast, we found no significant impact on the liver’s circadian clock in mice exposed to a short-term rotating shift condition.To investigate the impact of shift-work light conditions on the liver clock, we examined the circadian rhythms of canonical clock genes  collected under constant darkness after being exposed to simulated shift-work light conditions. We analyzed the circadian rhythms and determined their significance using CircaCompare with ap < 0.05. Our analysis revealed that majority of the analyzed clock genes in the skin exhibited rhythmic patterns. However, we observed a loss of rhythmicity inCry1 after exposure to short-term rotating shift and chronic jet-lag conditions. Additionally,Cry2 became arrhythmic when subjected to long-term rotating shift and chronic jet-lag conditions. Furthermore, we compared the circadian rhythms between the control group and the simulated shift-work groups, focusing on differences in phase (at least four hours), amplitude, and MESOR . Out of the eight genes analyzed in the skin, significant phase differences were observed in two genes (Arntl andCry2) following the short-term rotating shift condition. Four genes  exhibited significant phase differences following the long-term rotating shift condition, and one gene (Arntl) showed a significant phase difference following chronic jet-lag. Notably, most of these genes displayed a phase advance following simulated shift-work conditions compared to the control group.Arntl consistently exhibited dysregulation across all simulated shift conditions, particularly showing a phase delay in the long-term rotating shift scenario. Moreover, we observed alterations in amplitude:Cry2 andArntl exhibited reduced amplitude following short-term and long-term rotating shift conditions, respectively.Per2 displayed reduced amplitude in both rotating shift conditions, while no significant differences in amplitude were noted with chronic jet-lag. Regarding MESOR, two out of eight genes analyzed in each shift condition exhibited MESOR differences compared to the control group.Arntl displayed a significant increase in MESOR following exposure to short-term rotating shift and chronic jet-lag conditions, whilePer1 showed a significant increase following long-term rotating shift and chronic jet-lag. Additionally,Cry2 andPer2 exhibited a significant decrease in MESOR following exposure to the short-term and long-term rotating shift conditions, respectively. Overall, these results indicate that the circadian clock in the skin is impacted by all three simulated shift conditions and suggest that the skin clock is more sensitive to shift-work light conditions compared to the liver clock. Similar to our observations in the liver, the circadian clock in the skin is highly impacted in mice exposed to long-term rotating shift conditions.In addition to studying the liver, we also examined the expression of canonical clock genes in the dorsal skin of mice  and neuropsin (OPN5), have been identified as mediators of light entrainment from the retinal ganglion cells to the suprachiasmatic nucleus (SCN), the entrainment of liver clocks by light serves as a secondary event. In addition to synchronization from the central clock, feeding has been well demonstrated as a mechanism to reset liver clocks.– In our study, we provided mice with unrestricted access to food, allowing us to primarily observe changes arising from shift-work light conditions. The data from our study indicated that short-term exposure to rotating shift-light conditions did not significantly affect the circadian rhythms of liver clock genes. However, long-term exposure to rotating shifts resulted in circadian dysregulation, as evidence by loss of rhythmicity or significant differences in phase, amplitude, or MESOR in 7 out of 8 tested clock genes, and the jet-lag condition affected 3 out of 8 of the genes tested. These results suggest that liver clocks exhibit adaptability under less aggressive shift-work light conditions, possibly due to input from multiple environmental cues. Moreover, recent research has demonstrated that restricted feeding not only entrains liver clocks but also influences skin clocks in a distinct manner, highlighting an interesting interplay wherein a single stimulus can impact multiple systems.Unlike the skin, which directly senses external light stimuli, human melanocytes, keratinocytes, and dermal fibroblasts, and hair follicle stem cells. The presence of opsins within the skin cells indicates the possibility of light-sensing capabilities in the skin tissue and the potential influence of environmental light cues on core clock regulation. However, experimental verification of this hypothesis is still required.In our investigation, we observed significant disturbances in the circadian rhythm of canonical clock genes in the skin under various shift-work light conditions. Short-term exposure to rotating shift schedules and chronic jet-lag led to the disruption of circadian rhythmicity in 4 out of 8 tested canonical clock genes, while long-term exposure to rotating shifts affected 6 out of 8 of the genes, with most of these changes attributed to phase differences. Notably, unlike the liver, the circadian clock in the skin exhibited significant dysregulation across all shift-work light conditions examined in our study. This suggests that the skin’s circadian clock is more susceptible to the impact of shift-work light conditions compared to the clock in the liver. One potential explanation for this susceptibility is the involvement of opsins-mediated signaling. Opsins are a class of light sensing G protein-coupled receptors that trigger signaling cascades upon photosensation. Previous studies have identified the expression of various opsins in different models, including murine melanocytes,Cry1 and/orCry2), which are the primary repressors of the molecular clock and also involved in maintaining genome integrity of cells,, were significantly disrupted under simulated shift conditions in both tissues. However, in the case of short-term rotating shift specifically affecting the liver, the rhythmicity of these genes remained intact. Cryptochromes (CRYs), found across various species from plants to animals, conserved evolutionarily, function in photoreception and signal transduction. Their precise roles in mammalian circadian rhythm and light perception are complex, yet evidence suggests that CRYs may act as circadian photopigments, a possibility supported by experiments involving vitamin A depletion and mutations., Our findings align with this research, indicating thatCry genes could be more sensitive to altered light shifts than other clock genes. Consequently, their rhythms may be uniquely susceptible to disruption under simulated shift conditions.At the level of independent clock genes, we found that the circadian rhythms of cryptochrome genes .  The Authors should discuss this in their Limitations section, and how the outcomes may be different in females.In Figure 4, it is difficult to assess which oscillation characteristics are significantly different from control just by looking at the figure.  I am aware that full statistics are available in Extended Data Table 1, but it would be best if the Authors also figured out a way to represent, perhaps with a different shape (a star instead of a circle?) which datapoints were significantly different in Figure 4.In the Discussion, the Authors mainly focus on the observed differences between short-term and long-term rotating shift work, which is indeed important.  Equally important is the fact that many papers and groups use the jet lag protocol as a proxy for shift work, and the Authors demonstrate here that the outcomes of jet lag can be quite different from either short-term or long-term rotating shift work.  This is a very important finding that should be addressed in the Discussion. Overall, this is a well-designed and executed study with clear outcomes and interpretable results. I have several minor suggestions and comments that I believe will enhance the quality of the final manuscript.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Molecular cancer biology, lung biology, immunology, circadian rhythms, metabolismI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. et al. report that shift-work light affects the circadian rhythms of canonical clock genes in the liver and skin of mice. Although the authors have utilized multiple approaches to test their hypothesis, there remain areas where the methods section should also provide detailed information. Please refer to the following comments for more details.Describe in detail the process for collecting skin, which part of the skin tissue was collected?et al., 2021; Alves-Simoeset al., 2016; Fanet al., 2022). Meanwhile, the light intensity also affects the impact of chronic jet-lag.Is the selection of light intensity reasonable in the experiment? Multiple studies have shown that bright light can affect the circadian rhythm of activity, metabolism and clock gene expression (Bano-OtaloraMore information should be provided on Circadian rhythm analysis, especially the setting of parameters.Why use two housekeeping genes in the same tissue for clock gene relative quantification?Cry genes are preferentially altered under three light conditions.The author should discuss whyIn this manuscript, KoritalaIs the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?PartlyReviewer Expertise:circadian clock and metabolismI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


We sought to assess depression among healthcare workers (HCWs) in the context of COVID-19 in Lusaka Province, Zambia.This cross-sectional study is nested within a larger study, the Person-Centred Public Health for HIV Treatment in Zambia (PCPH), a cluster-randomised trial to assess HIV care and outcomes.The research was conducted in 24 government-run health facilities from 11 August to 15 October 2020 during the first wave of the COVID-19 pandemic in Lusaka, Zambia.We used convenience sampling to recruit HCW participants who were previously enrolled in the PCPH study, had more than 6 months’ experience working at the facility and were voluntarily willing to participate.We implemented the well-validated 9-question Patient Health Questionnaire (PHQ-9) to assess HCW depression. We used mixed-effects, adjusted Poisson regression to estimate the marginal probability of HCWs experiencing depression that may warrant intervention (PHQ-9 score ≥5) by healthcare facility.We collected PHQ-9 survey responses from 713 professional and lay HCWs. Overall, 334  HCWs recorded a PHQ-9 score ≥5, indicating the need for further assessment and potential intervention for depression. We identified significant heterogeneity across facilities and observed a greater proportion of HCWs with symptoms of depression in facilities providing COVID-19 testing and treatment services.Depression may be a concern for a large proportion of HCWs in Zambia. Further work to understand the magnitude and aetiologies of depression among HCWs in the public sector is needed to design effective prevention and treatment interventions to meet the needs for mental health support and to minimise poor health outcomes. These data represent important insights regarding the state of mental wellness among a large sample of healthcare workers (HCW) during a public health crisis in a resource-limited setting, where mental wellness, such as depression, is often not measured nor documented.We include a large number (n=24) of facilities to appreciate the heterogeneity in symptoms of depression among mental health in the sampled facilities in Lusaka Province of Zambia.An important limitation is lack of data on demographics of HCW respondents due to concern about stigma around mental wellness and potential to identify individuals given inclusion of clinics with small staff.Without pre-COVID-19 estimates of depression among HCWs in Lusaka, we are not able to show the association between mild-severe depression with the pandemic; however, we show that during the pandemic depression was high and attention to this population is justified to ensure a healthy HCW workforce.We employed convenience sampling in selecting participants which may limit the representativeness of study results to the wider population of HCWs.COVID-19 has caused a substantial global health hardship and the position of healthcare workers (HCWs) on the frontlines of the public health response places them at great risk for negative effects on health and well-being.8Several studies have been conducted to assess the mental wellness impact of the pandemic among HCWs; however, the majority of these studies focus on the continent of Asia and very little data are available for HCWs in Africa.12 18As part of a larger patient-centred care study, we collected facility-level measures to understand the context of care at 24 study sites. As a part of establishing care context we assessed mental health, specifically depression, using the 9-question Patient Health Questionnaire (PHQ-9) in the context of COVID-19 among HCWs in Lusaka Province, Zambia from 11 August 2020 to 15 October 2020.19This cross-sectional study of HCW depression is nested within a larger study, the Person-Centred Public Health for HIV Treatment in Zambia (PCPH), a cluster-randomised trial to assess HIV care and outcomes running from August 2018 to November 2021 across 24 government-funded health facilities in Lusaka Province . All PHQ-9 responses were collected between 11 August 2020 and 15 October 2020 among participating facilities that offered HIV care services and varied in size and location. Facilities were assigned codes to protect the identity of the HCW participants.We developed a cohort of HCWs who primarily provide HIV care at one of the 24 health facilities in the PCPH study. This was done by compiling all contact details from the HCWs who had, at the time of the introduction of the depression study, participated in the HCW survey component of the original PCPH study. The PCPH sample comprised both professional HCWs, including nurses, pharmacists, midwives, medical doctors and radiographers, and lay HCWs including treatment supporters and general workers. To augment participation in the PHQ-9 data collection, we expanded the PHQ-9 sample to include any HCW willing to participate and had ≥6 months’ working experience at the study facility. We informed the facility in charge of the PHQ-9 study opportunity who communicated it to staff members.Trained study research assistants visited healthcare facilities and discussed the study opportunity in person with available staff members. Those who expressed interest were screened for eligibility, offered the opportunity to verbally consent and participate immediately in the PHQ-9 study. As a part of the PCPH adaptation to the COVID-19 pandemic, we implemented the 9-question Patient Health Questionnaire (PHQ-9) to screen for presence and severity of depression. The PHQ-9 has been previously used in Zambia to screen for likelihood of depression,We followed the standard 27-point scoring system for the PHQ-9 to identify participants with scores consistent with minimal (0–4), mild (5–9), moderate (10–14), moderately severe 15–19) and severe depression (20-27). and seveThe parent study focused primarily on improving the patient experience at routine clinic visits through HCW training, mentorship and audit and feedback. The research approach and content is based on participatory research with HCWs, human-centred design workshops and stakeholder collaborations.A total of 713 HCWs from 24 facilities across Lusaka and Chongwe districts in the Lusaka Province were included in the analysis data set . The majOf the 713 responses, 231  reported mild depression (PHQ-9 score 5–9), and 81  reported moderate depression (PHQ-9 score 10–14). A total of 81 (11.4%) respondents had a PHQ-9 score corresponding to moderate depression across all but one facility, 17  respondents had PHQ-9 scores consistent with moderately severe depression across four facilities and 5  HCWs had PHQ-9 scores consistent with severe depression across five different facilities , table 210.1136/bmjopen-2022-069257.supp1Supplementary dataThough we observed facility-level mental health heterogeneity in the proportion of minimal and mild depression scores across clinics, the proportion of scores consistent with moderate depression remains relatively stable across facilities .Mixed-effects adjusted Poisson regression model did not reveal clinic size or month of survey as a significant predictor of PHQ-9 score ≥5 .We illustrate significant heterogeneity in the marginal probability of experiencing greater than minimal depression across facilities . NotablyWe found that a large proportion of the HCW population had a PHQ-9 score ≥5, indicating a need for follow-up to assess and improve mental well-being, 46.8% . Variation in depression outcomes ranged from 82.7%  at one of the two large COVID-19 treatment facilities to 28.4%  at a medium-sized healthcare facility.Our study shows that HCWs working at the COVID-19 treatment facility had a higher marginal probability of experiencing mild to moderately severe depression. This is consistent with past studies showing that frontline HCWs working in clinics managing diseases considered to be highly infectious, such as COVID-19 treatment centres, were more prone to developing depression and other mental disorders compared to their counterparts in other departments.1 33–35Further research is needed to understand the heterogeneity in proportions of HCWs with depression. It may be associated variation in HCW access to resources, such as PPE, hand hygiene station/station supplies at clinic entrance and knowledge about the COVID-19 response in Zambia. While the response to the COVID-19 pandemic was standardised, to a certain extent, by guidance from the provincial and zonal levels, the culture and leadership unique to each facility might have played a key role in the HCW experience, contributing to PHQ-9 score distribution differences across facilities.As we continue to recognise the mental health services gap across many populations in resource-limited settings we give evidence here to support prioritisation of HCWs, especially during public health shocks/emergencies like that presented by COVID-19. Presence of depression among HCWs could lead to poor health outcomes for the HCW workforce and have a sort of ‘knock-on’ effect negatively impacting the quality of care provided to patients.42 43This study had some potential limitations. First, we did not collect participant demographics such as age, sex and marital status to protect participant privacy, espeically at small clinics, where staff are few. Data for these potential confounders might help to better understand associations with depression. Second, we employed convenience sampling in selecting participants. This sampling approach may limit the representativeness of the study results to the wider population of HCWs. Finally, without pre-COVID-19 estimates of depression among HCWs in Lusaka, we are not able to show the association between mild-severe depression with the pandemic; however, we show that during the pandemic the depression was high and attention to this population is justified to ensure a healthy HCW workforce. Additionally, though potentially higher than a non-pandemic baseline, these results may be useful as subsequent measures of depression and mental wellness are collected among HCWs.Depression is a common public health problem; our study demonstrates that HCWs in Zambia may suffer from a high prevalence of depressive symptoms that will require additional clinical follow-up. Routine mental health wellness is important to better understand the role that the COVID-19 pandemic may have had on depression among HCWs in Zambia. Furthermore, support for HCW mental wellness can serve to accelerate destigmatising mental health issues and improve the quality of care provided across healthcare centres in Zambia.