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> Procedures > Therapeutic or Preventive Procedure > Filtering Surgery

Filtering Surgery

Filtering surgery is a medical procedure performed to improve fluid drainage and reduce intraocular pressure in the eye.
This technique is commonly used to treat conditions like glaucoma, where increased pressure can damage the optic nerve and lead to vision loss.
PubCompare.ai, an AI-driven platform, can help optimize your research protocols for filtering surgery by effortlessly locating the best procedures from literature, pre-prints, and patents using advanced comparisons.
Streamline your research process and make informed decisions with PubCompare.ai's powerful insights, which can help you idenfity the most effective and efficient filtering surgery techniques.

Most cited protocols related to «Filtering Surgery»

1
Functional Brain Network Analysis
Cited 10 times
Many different approaches exist to determine functional connectivity from time series data (Pereda et al., 2005 (link)). Different methods employ distinct coupling measures (e.g., linear or nonlinear measures) and different strategies for assigning network edges. In this work we utilize two measures of linear coupling: the cross correlation and coherence. We outline here our particular data analysis approach; a detailed discussion, including the statistical properties and simulation results for the cross correlation measure, may be found in (Kramer et al., 2009 ). Before applying the coupling analysis, we process the ECoG data from each seizure and subject in the following way. For the cross correlation analysis, we first notch filter (third order Butterworth, zero-phase digital filtering) the data at 60 Hz and 120 Hz to remove line noise, high pass filter the data above 1 Hz to avoid slow drift, and low pass filter the data below 150 Hz to avoid higher frequency line noise harmonics. For the coherence measure we do not perform these filtering operations, and instead focus on frequency intervals that exclude narrowband noise peaks and slow drift oscillations. Next, we subtract the average reference from each electrode to reduce the contribution of the reference electrode to coupling (Towle et al., 1999 (link)). Then we divide the ECoG data into non-overlapping windows of duration 1.024s. We choose ~1s intervals here to balance the requirements of approximate stationarity of the time series (requiring short epochs) and of sufficient data to allow accurate coupling estimates (requiring long epochs). Finally, we normalize the data from each electrode within each window to have zero mean and unit variance.
With the data processed in this way, we construct functional networks for each window in three steps. We briefly describe these steps here; a complete discussion may be found in (Kramer et al., 2009 ). In the first step we choose two electrodes, and apply either the cross correlation or the coherence to the ECoG data. For the correlation, we select the maximum correlation within time delays of +/− 250 ms. This interval of delays allows an assessment of the variance in the cross correlation over time delays which is used to calculate the significance of the correlation (Kramer et al., 2009 ). For the coherence, we use the multitaper method with a time bandwidth product of 5 and 8 tapers. For the choices of window size (~1s) and time bandwidth product (5), the half-bandwidth is 5 Hz. We therefore analyze the coherence in evenly spaced 10 Hz bands (the full bandwidth) - {5–15 Hz, 15–25 Hz, 25–35 Hz, and 35–45 Hz} - for all electrode pairs. These bands cover traditional oscillatory classes: 5–15 Hz, theta and alpha; 15–25 Hz, beta; 25–35 Hz and 35–45 Hz, gamma (Buzsaki & Draguhn, 2004 (link)). Low frequencies are omitted to avoid low frequency drift in the data. Second, we determine the statistical significance of these coupling results through analytic procedures (Mitra & Bokil, 2008 ; Kramer et al., 2009 ). Third, we correct for multiple significance tests using a linear step-up procedure controlling the false detection rate (FDR) with q=0.05. For this choice of q, 5% of the network connections are expected to be falsely declared (Benjamini & Hochberg, 1995 ). This procedure results in a thresholding of the significance tests (i.e., the p-values) of the coupling measure - not of the correlation or coherence value itself - for each interval of data (Kramer et al., 2009 ). The resulting network in each window possesses an associated measure of uncertainty, namely the expected number of edges incorrectly declared present.
Kramer M.A., Eden U.T., Lepage K.Q., Kolaczyk E.D., Bianchi M.T, & Cash S.S. (2011). Emergence of Persistent Networks in Long-Term Intracranial EEG Recordings. The Journal of Neuroscience, 31(44), 15757-15767.
Publication 2011
Crossing Over, Genetic Electrocorticography EPOCH protocol Filtering Surgery Fingers Gamma Rays Seizures
2
Fetal Cardiac Signal Extraction from MCG
Cited 10 times
In this paper, the summed RHA method, FastICA, RHA applied to two or more FastICA components, and simple filtering are considered postprocessing steps designed to simplify the extraction of the fetal R-waves. Since the maternal cardiac signal is dominant over the fetal signal, the maternal MCG was first removed from all SQUID channels by a signal space projection algorithm [31 ] before applying any of the methods tested. To further reduce the influence of the residual maternal MCG, only data from the lower 82 channels of the SARA system, far from the maternal heart but nearer to the fetal heart, were used in our analysis. To be clear, the FastICA was not used to separate the fetal and maternal MCGs in this study, but rather it was only used to separate the fMCG from background signal. After removal of the maternal MCG, each dataset was postprocessed in four ways: 1) compute the RHA of each channel and sum (Hilbert); 2) apply the FastICA and select the dominant fetal component (ICA); 3) compute the RHA of all FastICA components containing fMCG and sum (ICA+Hilbert); and 4) manually select the channel with the largest fMCG signal (filtered). The label “filtered” refers to the 1–100 Hz bandwidth imposed by the initial filtering operations. Ideally, after applying any of the earlier postprocessing methods, the fiducial points may be identified by simply extracting all of the maxima and testing which maxima fall above a selected threshold.
To illustrate this process, the results from a single recording are presented in Fig. 4. Here, the local maxima from each method were extracted and normalized by the largest positive value, and then, used to build the histograms shown in Fig. 4(A)–(D). For clarity, the histograms were scaled by taking the log(n+1) of each histogram bin. Fig. 4(E)–(H) shows the resulting RR intervals obtained after selecting a threshold between 0 and 1 and then extracting the remaining maxima. For this dataset, the ICA and Hilbert method (RHA) separate the maxima into two nonoverlapping distributions, as shown in Fig. 4(B) and (D), where the histograms indicate a completely resolved distribution of R-wave maxima separated from the distribution of lower amplitude maxima arising from noise and/or other fMCG components. Fig. 4(F) and (H) are the graphs of the corresponding RR intervals for any value of threshold falling between the two resolved distributions. In contrast, Fig. 4(A) and (C) shows overlapping distributions. In these two cases, a single threshold cannot be selected that cleanly separates the maxima of R-waves from maxima originating from other sources. As an example, Fig. 4(E) shows an upward spike in the graph of RR intervals near the beginning of the dataset indicating that at least one true R-wave maxima fell below the threshold. We refer to this case as a “missed” beat. Fig. 4(G) shows several downward spikes indicating that a few maxima were falsely classified as R-waves. We refer to this case as “extra” beat.
Wilson J.D., Govindan R.B., Hatton J.O., Lowery C.L, & Preissl H. (2008). Integrated Approach for Fetal QRS Detection. IEEE transactions on bio-medical engineering, 55(9), 2190-2197.
Publication 2008
Care, Prenatal Fetal Heart Filtering Surgery Heart Squid
3
CNN-based Multimodal Prediction Protocol
Cited 8 times
We used CNNs with multiple cell structures that have two one-dimensional convolution layers, one pooling layer and one dropout layer. Convolution layers are designed to extract features with high-dimensional abstract representation. The pooling layer limits the number of model parameters tractable by pooling operations. The dropout layer prevents overfitting of the model by randomly setting some of the input units to a value of 0. Four prediction methods had been established based on four different network structures composed by the cell structures mentioned above. One-hot encoding data were fed into the network with four cell structures and fully connected layers as input, while neighboring methylation state encoding data, RNA word embedding data, and Gene2vec processing data were fed into networks with two cell structures (Fig. 10). The final result was obtained by a voting strategy from the four prediction probabilities.
Taking an example of the one-hot coding sequence, the input data matrix Xn was first fed into a 1D-convolutional layer, which used a convolutional filter Wf ∈ RH, where H is the length of the filter vector. The output feature Ai at the ith position was computed by
Ai=ReLU(∑h=1H⁡WfXn,i+h+bf),
where ReLU(x) = max(0, x) is the rectified linear unit function and bf ∈ R is a bias (Mairal et al. 2014 ). These convolutional operations are similar to data block of H length in sequence filtered by a sliding filter window at each ith position.
Next, a max. pooling layer was used for reduction of the dimensions of output data generated by the multiple convolutional filter operations. A max. pooling layer is a form of nonlinear downsampling achieved by outputting the maximum of each subregion.
To reduce overfitting, we added a dropout layer in which individual nodes were either “dropped out” from the network with probability 1 − P or kept with probability P at each training stage. This not only prevented overfitting, but also led to integration of various deformed network structures to generate more robust features that are more generalizable to new data.
Finally, a flattening layer that “flattened” the input data was used, which transformed multidimensional data into a single dimension. Fully connected layers with an ReLU activation function and output layer predict the binary classification probability with activation function as follows (Han and Moraga 1995 ):
y^(x)=sigmoid(x)=(11+e−x).
Zou Q., Xing P., Wei L, & Liu B. (2019). Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA. RNA, 25(2), 205-218.
Publication 2019
Cardiac Arrest Cellular Structures Cloning Vectors Filtering Surgery Methylation Open Reading Frames Sigmoid Colon
4
Evaluating Speech Reconstruction Accuracy
Cited 7 times
Reconstruction accuracy was quantified separately for each stimulus component by computing the correlation coefficient (Pearson's r) between the reconstructed and original stimulus component. For each participant, this yielded 32 individual correlation coefficients for the 32 channel spectrogram model and 60 correlation coefficients for the 60 channel rate-scale modulation model (defined in Speech Stimuli). Overall reconstruction accuracy is reported as the mean correlation over all stimulus components.
To make a direct comparison of modulation and spectrogram-based accuracy, the reconstructions need to be compared in the same stimulus space. The linear spectrogram reconstruction was therefore projected into the rate-scale modulation space (using the modulation filterbank as described in Speech Stimuli). This transformation provides an estimate of the modulation content of the spectrogram reconstruction and allows direct comparison with the modulation reconstruction. The transformed reconstruction was then correlated with the 60 rate-scale components of the original stimulus. Accuracy as a function of rate (Figure 5A) was calculated by averaging over the scale dimension. Positive and negative rates were also averaged unless otherwise shown. Comparison of reconstruction accuracy for a subset of data in the full rate-scale-frequency modulation space yielded similar results. To impose additivity and approximate a normal sampling distribution of the correlation coefficient statistic, Fisher's z-transform was applied to correlation coefficients prior to tests of statistical significance and prior to averaging over stimulus channels and participants. The inverse z-transform was then applied for all reported mean r values.
To visualize the modulation-based reconstruction in the spectrogram domain (Figure 7B), the 4-D modulation representation needs to be inverted [18] (link). If both magnitude and phase responses are available, the 2-D spectrogram can be restored by a linear inverse filtering operation [18] (link). Here, only the magnitude response is reconstructed directly from neural activity. In this case, the spectrogram can be recovered approximately from the magnitude-only modulation representation using an iterative projection algorithm and an overcomplete set of modulation filters as described in Chi et al. [18] (link). Figure 7B displays the average of 100 random initializations of this algorithm. This approach is subject to non-neural errors due to the phase-retrieval problem (i.e., the algorithm does not perfectly recover the spectrogram, even when applied to the original stimulus) [18] (link). Therefore, quantitative comparisons with the spectrogram-based reconstruction were performed in the modulation space.
Reconstruction accuracy was cross-validated and the reported correlation is the average over all resamples (see Cross-Validation) [53] (link). Standard error is computed as the standard deviation of the resampled distribution [17] . The reported correlations are not corrected to account for the noise ceiling on prediction accuracy [16] (link), which limits the amount of potentially explainable variance. An ideal model would not achieve perfect prediction accuracy of r = 1.0 due to the presence of random noise that is unrelated to the stimulus. With repeated trials of identical stimuli, it is possible to estimate trial-to-trial variability to correct for the amount of potentially explainable variance [56] (link). In the experiments reported here, a sufficient number of trial repetitions (>5) was generally unavailable for a robust estimate, and uncorrected values are therefore reported.
Pasley B.N., David S.V., Mesgarani N., Flinker A., Shamma S.A., Crone N.E., Knight R.T, & Chang E.F. (2012). Reconstructing Speech from Human Auditory Cortex. PLoS Biology, 10(1), e1001251.
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Publication 2012
Filtering Surgery Nervousness Reconstructive Surgical Procedures Speech
5
Glaucoma Diagnosis Protocol: Comprehensive Approach
Cited 6 times
Diagnosis of glaucoma was based on three aspects: the eye fundus, the visual field results, and the IOP. At the beginning, subjects were categorized as definite, probable, possible, or no glaucoma based on eye fundus. Subsequently, for the probable and possible subjects, if there were glaucomatous visual field defects and if the IOP was >21 mmHg, they would be diagnosed as glaucoma.
Glaucomatous visual field defects had at least two of the following characteristics: (1) a cluster of three points with a probability less than l% on a pattern deviation map in at least one hemifield, including at least one point with a probability less than 1%; or a cluster of two points with a probability less than 1%. (2) glaucoma hemifield test results outside 99% of the age-specific normal limits. (3) pattern standard deviation outside 95% of the normal limits.
Specifically, the eye fundus reading followed the following process. Firstly, four ophthalmologists from Beijing Tongren Hospital (YZ, JH, QZ, and ZG) reviewed disc photographs for vertical cup/disc ratios, rim of optic disc, nerve fiber layer defect, and optic disk hemorrhage. Secondly, independent review of the finding was carried out by three senior glaucoma specialists (TR, BSW, and YBL), classified the patients according to the same definitions. If the results differed among three specialists, a third independent reading was conducted by another glaucoma specialist (DSF). The final diagnosis was determined by another glaucoma specialist (NLW) if some confused diagnosis still existed in the third step.
Glaucoma was also diagnosed as present in cases where the optic nerve was not visible due to media opacity and the VA was <20/400 and the IOP was >99.5th percentile, or the VA was <20/400 and the eye had evidence of prior glaucoma filtering surgery, or medical records were available confirming glaucomatous visual morbidity.
Cao K., Hao J., Zhang Y., Hu A.L., Yang X.H., Li S.Z., Wang B.S., Zhang Q., Hu J.P., Lin C.X., Yusufu M, & Wang N.L. (2019). Design, methodology, and preliminary results of the follow-up of a population-based cohort study in rural area of northern China: Handan Eye Study. Chinese Medical Journal, 132(18), 2157-2167.
Publication 2019
Filtering Surgery Fundus Oculi Glaucoma Hemorrhage Nerve Fibers Ophthalmologists Optic Disk Optic Nerve Patients

Most recents protocols related to «Filtering Surgery»

1
Raman Spectroscopy Setup and Characterization
RS measurements were taken in a custom system at Bogazici University Department of Physics. We built the system using three main parts: a diode laser (785 nm, 100 mW, LaserGlow), a spectrometer (QE-Pro, Ocean), and a microscope rigid-body microscope (Nikon Ti). The laser beam was cleaned up with a bandpass filter (Semrock), and the output beam was transmitted through a spatial filter to obtain a single longitudinal mode operation. The cleaned-up beam was sent to the first dichroic mirror (LP805, Thorlabs) and reflected to the other (SP 750, Thorlabs). The reflected beam from the last dichroic mirror was steered to the microscope objective (10×, 0.25 NA, Olympus). The backscattered beam (Rayleigh and Raman photons) was collected via the same light path with 180°C geometry. Finally, the Rayleigh photons were filtered at the first dichroic mirror. The Raman beam was sent to the focusing lens after transmission from a Raman edge filter (Semrock) for further Rayleigh filtering operation. The Raman beam was focused into the multimode fiber (0.22 NA, Thorlabs) using a fiber collimation package (Thorlabs). The coupled beam was sent to the spectrometer, and the spectra were visualized using the appropriate software (Oceanview).
SEN A., Kecoglu I., Ahmed M., Parlatan U, & Unlu M.B. (2023). Differentiation of advanced generation mutant wheat lines: Conventional techniques versus Raman spectroscopy. Frontiers in Plant Science, 14, 1116876.
Full text: Click here
Publication 2023
A Fibers Fibrosis Filtering Surgery Human Body Lasers, Semiconductor Light Microscopy Muscle Rigidity Transmission, Communicable Disease
2
Gene Expression Data Processing
Raw expression data from the 224 selected samples were subjected to background correction, quantile normalization, and log2 transformation through the RMA algorithm from the affy R package (version 1.74.0). A filtering operation was applied to reduce the probes that exhibited low variation and a consistently low signal across samples. The median expression of the dataset was calculated and returned a median value of 7.2, thus a probe was kept if the probe expression is above the median in more than 10 samples. The probe identification numbers were then transformed into official gene symbols and duplicate probes were deleted.
Eshibona N., Livesey M., Christoffels A, & Bendou H. (2023). Investigation of distinct gene expression profile patterns that can improve the classification of intermediate-risk prognosis in AML patients. Frontiers in Genetics, 14, 1131159.
Full text: Click here
Publication 2023
Filtering Surgery Genes
3
CNN-based Feature Extraction for Image Classification
The first few layers of the CNN network structure are used as a feature extractor to automatically obtain the image features through supervised training, which are detected by the SoftMax function in the final layer [22 (link)]. Figure 6 presents the CNN structure.
As can be seen from Figure 6, there are eight layers in CNN in total. The first five layers are alternating convolution layers and Max Pooling layers, and the remaining three are fully connected layers. The input image of CNN is the harmonic spectrum and impact spectrum generated by HPSS separation, including the original signal spectrum. The images are unified to 256 ∗ 256 and input into the first convolution filter. A filter operation is performed on the input image by 96 kernels of 11 ∗ 11 with a stride of 4 pixels in the first convolution layer due to the distance between the Receptive Field centers of adjacent neurons in the same core map [23 ]. Then, the Max Pooling layer uses the output of the first convolutional layer as the input and performs filtering operations with 96 kernels of size 3 ∗ 3. After unifying the input size, the second convolutional layer performs a filtering operation on the output of the Max Pooling layer using 256 kernels of 5 ∗ 5. The third, fourth, and fifth convolutional layers are connected to each other. There is no pooling or normalization layer in between. The third convolutional layer has a total of 384 kernels of size 3 ∗ 3 connected to the second convolutional layer's output [24 (link)]. The fourth convolutional layer has a total of 384 kernels of size 3 ∗ 3, and the fifth convolutional layer has a total of 256 kernels of size 3 ∗ 3. Finally, 256 feature maps of size 6 ∗ 6 are obtained through these five convolutional layers. These feature maps are fed to three fully connected layers, each with 4096, 1,000, and 10 neurons. The final detection result is output by the last fully connected layer [25 (link)].
, & Wang X. (2023). Music Similarity Detection Guided by Deep Learning Model. Computational Intelligence and Neuroscience, 2023, 1263620.
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Publication 2023
Filtering Surgery Hantavirus Infections Microtubule-Associated Proteins Neurons Vision
4
EPViz: Interactive EEG Visualization and Filtering

Fig 1 illustrates the EPViz graphical user interface. The “Select File” button allows the user to load an EDF file containing multi-channel EEG data. The popup window asks the user to select which channels to plot. We have included the standard 10-10, 10-20 and bipolar 10-20 montages as preset selections. The user also has the option to load a custom EEG montage via a separate text file.
The EEG signals appear in the main display pane. Signals from the default montages are color-coded according to hemisphere (red for left, blue for right, and green for the midline). This is in contrast to EDFBrowser, which defaults to plotting all signals in black. Users can change the ordering and number of plotted signals using the “Change Signals” button. Annotations in the EDF files are plotted as “Notes” at the bottom of the display pane. These are particularly relevant for clinical EEG data. Users can vary the time scale of the plot (1, 5, 10, 20, 25, 30, or 45 seconds) using the “Change Window Size” button. Likewise, they can change the intensity scale via the “Change Amplitude” button. Finally, the “Open Zoom” button allows the user to zoom in on a selected region of the plotting window.
EPViz includes basic filtering operations. The high- and low-pass parameters, implemented using the SciPy library, can be set in the “Change Filter” pop-up. To allow for real-time updating, only the region shown on the screen is filtered. These filtering operations mimic those used in epilepsy and BCI applications. More complex preprocessing, such as ICA, should be done offline prior to loading the file into EPViz.
Currey D., Craley J., Hsu D., Ahmed R, & Venkataraman A. (2023). EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG. PLOS ONE, 18(2), e0282268.
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Publication 2023
cDNA Library Epilepsy Filtering Surgery Neoplasm Metastasis
5
Frequency Hopping Signal Timing Detection
Let x(t) be defined as the non-stationary frequency hopping analytic signal: xa(t)=ei2πf1t[u(t)−u(t−Thop)]+ei2πf2t[u(t−Thop+1)−u(t−T)], 0≤t≤T,
where Thop is the hopping time at which point the message frequency hops from frequency f1 to f2, and u(t) is the unit step function defined as: u(t)={1 ,for t≥00,for t<0
Substituting Equation (2) into Equation (1) leads to [3 ] (p. 49): TCF(t,τ)={ei2πf1τ[u(t+τ2)[u(t−τ2)−u(t−τ2−Thop)]+u(t+τ2−Thop)[u(t−τ2−Thop)−u(t−τ2)]]+ei2πf2τ[u(t+τ2−Thop+1)[u(t−τ2−Thop+1)−u(t−τ2−T)]+u(t+τ2−T)[u(t−τ2−T)−u(t−τ2−Thop+1)]]ei2π[(f2−f1)t+(f1+f22)τ][u(t+τ2−Thop+1)[u(t−τ2)−u(t−τ2−Thop)]+u(t+τ2−T)[u(t−τ2−Thop)−u(t−τ2)]]=TCF1(t,τ)+TCF2(t,τ)+TCF12(t,τ).
Terms TCF1(t,τ), TCF2(t,τ), and TCF12(t,τ), respectively, for the corresponding 1st, 2nd, and 3rd terms included in Equation (4). The unit step expressions confined the boundaries of these three terms and formed non-overlapping regions that composed a complete TCF expression. Note that the phase of the TCF1(t,τ) and TCF2(t,τ) terms expressed as a function of the variable “t” are constant and equal to 2πf1τ and 2πf2τ, respectively, while the phase contribution contained in term TCF12(t,τ) varies in terms of both variables t and τ. The phase expression of three terms form the boundaries in a shape of equilateral triangle, as shown in Figure 2. The TCF phase expressed as a function of “t” for a time difference “τ” and exhibited the change in phase continuity between auto terms and cross terms.
Therefore, the TCF phase expressed as a function of “t” for a time difference “τ” exhibits change in phase continuity between auto terms and cross terms. Figure 2 illustrates the behavior of the phase of the TCF matrix computed from the signal xa(t) in Equation (2), where f1 = 15 Hz, f2 = 45 Hz the hopping time Thop = 130, and the total signal duration T = 300, which was also the experimentally frequency-agile-signal design used in the study. Figure 2 showed that the hopping time signature was revealed at the left vertex of the triangle trajectory of TCF12(t,τ) region and the instant Thop could resolve by indicating the intercepted time index from the two diagonal lines of the boundary in TCF12(t,τ). The boundary of cross term TCF12(t,τ) held ±45° orientated lines between two auto terms of TCF1(t,τ) and TCF2(t,τ).
The changes in TCF phase as a function of “t” are computed to automatically detect edges for resolving Thop. The process contains a one-dimensional wavelet transform that emphasizes discontinuous edges and a two-dimensional dual-diagonal operator (DDO) for morphological matched filtering operations. The timing signature enhancement and morphological operation used in this study were introduced in Section 3 and Section 4.
The FHSS timing detection and estimation scheme was processed by two main phases, illustrated in Figure 3.
Phase 1: Timing signature enhancement applying to the region boundaries of TCF phase terms.
Phase 2: Applying morphological operations in edge finding and denoise proceed by matched oriented kernel operation and resolving timing information based on binary images.
Cheng Y.P., Chang C.H, & Chen J.C. (2023). Low-False-Alarm-Rate Timing and Duration Estimation of Noisy Frequency Agile Signal by Image Homogeneous Detection and Morphological Signature Matching Schemes. Sensors (Basel, Switzerland), 23(4), 2094.
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Publication 2023
ARID1A protein, human Filtering Surgery HNF1A protein, human Humulus TCF12 protein, human

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More about "Filtering Surgery"

Filtering surgery, also known as trabeculectomy, is a medical procedure performed to improve fluid drainage and reduce intraocular pressure (IOP) in the eye.
This technique is commonly used to treat conditions like glaucoma, where increased IOP can damage the optic nerve and lead to vision loss.
Glaucoma is a group of eye conditions characterized by progressive damage to the optic nerve, often caused by high IOP.
The surgical process involves creating a small opening in the sclera (the white part of the eye) to allow excess fluid to drain from the eye, thereby reducing IOP.
This procedure is typically performed when other glaucoma treatments, such as eye drops or laser therapy, have not been effective in controlling IOP.
PubCompare.ai, an AI-driven platform, can help optimize your research protocols for filtering surgery by effortlessly locating the best procedures from literature, preprints, and patents using advanced comparisons.
The platform can also provide insights into related techniques, such as canaloplasty, which aims to improve the natural drainage system of the eye, and minimally invasive glaucoma surgery (MIGS), which uses smaller incisions and less invasive methods to lower IOP.
Streamlining your research process with PubCompare.ai can help you make informed decisions about the most effective and efficient filtering surgery techniques, taking into account factors such as surgical outcomes, complication rates, and patient satisfaction.
By leveraging the platform's powerful insights, you can stay up-to-date with the latest advancements in the field and ensure your research protocols are optimized for success.